diff --git a/Modules/DiffusionImaging/DiffusionCmdApps/Tractography/StreamlineTractography.cpp b/Modules/DiffusionImaging/DiffusionCmdApps/Tractography/StreamlineTractography.cpp index e486ae680f..f59e8447da 100755 --- a/Modules/DiffusionImaging/DiffusionCmdApps/Tractography/StreamlineTractography.cpp +++ b/Modules/DiffusionImaging/DiffusionCmdApps/Tractography/StreamlineTractography.cpp @@ -1,563 +1,554 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #define _USE_MATH_DEFINES #include const int numOdfSamples = 200; typedef itk::Image< itk::Vector< float, numOdfSamples > , 3 > SampledShImageType; /*! \brief */ int main(int argc, char* argv[]) { mitkCommandLineParser parser; parser.setTitle("Streamline Tractography"); parser.setCategory("Fiber Tracking and Processing Methods"); parser.setDescription("Perform streamline tractography"); parser.setContributor("MIC"); // parameters fo all methods parser.setArgumentPrefix("--", "-"); parser.beginGroup("1. Mandatory arguments:"); parser.addArgument("", "i", mitkCommandLineParser::StringList, "Input:", "input image (multiple possible for 'DetTensor' algorithm)", us::Any(), false, false, false, mitkCommandLineParser::Input); parser.addArgument("", "o", mitkCommandLineParser::String, "Output:", "output fiberbundle/probability map", us::Any(), false, false, false, mitkCommandLineParser::Output); - parser.addArgument("algorithm", "", mitkCommandLineParser::String, "Algorithm:", "which algorithm to use (DetPeaks; ProbPeaks; DetTensor; ProbTensor; DetODF; ProbODF; DetRF; ProbRF)", us::Any(), false); + parser.addArgument("type", "", mitkCommandLineParser::String, "Type:", "which tracker to use (Peaks; Tensor; ODF; RF)", us::Any(), false); + parser.addArgument("probabilistic", "", mitkCommandLineParser::Bool, "Probabilistic:", "Probabilistic tractography", us::Any(false)); parser.endGroup(); parser.beginGroup("2. Seeding:"); parser.addArgument("seeds", "", mitkCommandLineParser::Int, "Seeds per voxel:", "number of seed points per voxel", 1); parser.addArgument("seed_image", "", mitkCommandLineParser::String, "Seed image:", "mask image defining seed voxels", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.addArgument("trials_per_seed", "", mitkCommandLineParser::Int, "Max. trials per seed:", "try each seed N times until a valid streamline is obtained (only for probabilistic tractography)", 10); parser.addArgument("max_tracts", "", mitkCommandLineParser::Int, "Max. number of tracts:", "tractography is stopped if the reconstructed number of tracts is exceeded", -1); parser.endGroup(); parser.beginGroup("3. Tractography constraints:"); parser.addArgument("tracking_mask", "", mitkCommandLineParser::String, "Mask image:", "streamlines leaving the mask will stop immediately", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.addArgument("stop_image", "", mitkCommandLineParser::String, "Stop ROI image:", "streamlines entering the mask will stop immediately", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.addArgument("exclusion_image", "", mitkCommandLineParser::String, "Exclusion ROI image:", "streamlines entering the mask will be discarded", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.addArgument("ep_constraint", "", mitkCommandLineParser::String, "Endpoint constraint:", "determines which fibers are accepted based on their endpoint location - options are NONE, EPS_IN_TARGET, EPS_IN_TARGET_LABELDIFF, EPS_IN_SEED_AND_TARGET, MIN_ONE_EP_IN_TARGET, ONE_EP_IN_TARGET and NO_EP_IN_TARGET", us::Any()); parser.addArgument("target_image", "", mitkCommandLineParser::String, "Target ROI image:", "effact depends on the chosen endpoint constraint (option ep_constraint)", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.endGroup(); parser.beginGroup("4. Streamline integration parameters:"); parser.addArgument("sharpen_odfs", "", mitkCommandLineParser::Bool, "SHarpen ODFs:", "if you are using dODF images as input, it is advisable to sharpen the ODFs (min-max normalize and raise to the power of 4). this is not necessary for CSD fODFs, since they are narurally much sharper."); parser.addArgument("cutoff", "", mitkCommandLineParser::Float, "Cutoff:", "set the FA, GFA or Peak amplitude cutoff for terminating tracks", 0.1); parser.addArgument("odf_cutoff", "", mitkCommandLineParser::Float, "ODF Cutoff:", "threshold on the ODF magnitude. this is useful in case of CSD fODF tractography.", 0.0); parser.addArgument("step_size", "", mitkCommandLineParser::Float, "Step size:", "step size (in voxels)", 0.5); parser.addArgument("min_tract_length", "", mitkCommandLineParser::Float, "Min. tract length:", "minimum fiber length (in mm)", 20); parser.addArgument("angular_threshold", "", mitkCommandLineParser::Float, "Angular threshold:", "angular threshold between two successive steps, (default: 90° * step_size, minimum 15°)"); parser.addArgument("loop_check", "", mitkCommandLineParser::Float, "Check for loops:", "threshold on angular stdev over the last 4 voxel lengths"); parser.endGroup(); parser.beginGroup("5. Tractography prior:"); parser.addArgument("prior_image", "", mitkCommandLineParser::String, "Peak prior:", "tractography prior in thr for of a peak image", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.addArgument("prior_weight", "", mitkCommandLineParser::Float, "Prior weight", "weighting factor between prior and data.", 0.5); parser.addArgument("dont_restrict_to_prior", "", mitkCommandLineParser::Bool, "Don't restrict to prior:", "don't restrict tractography to regions where the prior is valid.", us::Any(false)); parser.addArgument("no_new_directions_from_prior", "", mitkCommandLineParser::Bool, "No new directios from prior:", "the prior cannot create directions where there are none in the data.", us::Any(false)); parser.addArgument("prior_flip_x", "", mitkCommandLineParser::Bool, "Prior Flip X:", "multiply x-coordinate of prior direction by -1"); parser.addArgument("prior_flip_y", "", mitkCommandLineParser::Bool, "Prior Flip Y:", "multiply y-coordinate of prior direction by -1"); parser.addArgument("prior_flip_z", "", mitkCommandLineParser::Bool, "Prior Flip Z:", "multiply z-coordinate of prior direction by -1"); parser.endGroup(); parser.beginGroup("6. Neighborhood sampling:"); parser.addArgument("num_samples", "", mitkCommandLineParser::Int, "Num. neighborhood samples:", "number of neighborhood samples that are use to determine the next progression direction", 0); parser.addArgument("sampling_distance", "", mitkCommandLineParser::Float, "Sampling distance:", "distance of neighborhood sampling points (in voxels)", 0.25); parser.addArgument("use_stop_votes", "", mitkCommandLineParser::Bool, "Use stop votes:", "use stop votes"); parser.addArgument("use_only_forward_samples", "", mitkCommandLineParser::Bool, "Use only forward samples:", "use only forward samples"); parser.endGroup(); parser.beginGroup("7. Tensor tractography specific:"); parser.addArgument("tend_f", "", mitkCommandLineParser::Float, "Weight f", "weighting factor between first eigenvector (f=1 equals FACT tracking) and input vector dependent direction (f=0).", 1.0); parser.addArgument("tend_g", "", mitkCommandLineParser::Float, "Weight g", "weighting factor between input vector (g=0) and tensor deflection (g=1 equals TEND tracking)", 0.0); parser.endGroup(); parser.beginGroup("8. Random forest tractography specific:"); parser.addArgument("forest", "", mitkCommandLineParser::String, "Forest:", "input random forest (HDF5 file)", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.addArgument("use_sh_features", "", mitkCommandLineParser::Bool, "Use SH features:", "use SH features"); parser.endGroup(); parser.beginGroup("9. Additional input:"); parser.addArgument("additional_images", "", mitkCommandLineParser::StringList, "Additional images:", "specify a list of float images that hold additional information (FA, GFA, additional features for RF tractography)", us::Any(), true, false, false, mitkCommandLineParser::Input); parser.endGroup(); parser.beginGroup("10. Misc:"); parser.addArgument("flip_x", "", mitkCommandLineParser::Bool, "Flip X:", "multiply x-coordinate of direction proposal by -1"); parser.addArgument("flip_y", "", mitkCommandLineParser::Bool, "Flip Y:", "multiply y-coordinate of direction proposal by -1"); parser.addArgument("flip_z", "", mitkCommandLineParser::Bool, "Flip Z:", "multiply z-coordinate of direction proposal by -1"); parser.addArgument("no_data_interpolation", "", mitkCommandLineParser::Bool, "Don't interpolate input data:", "don't interpolate input image values"); parser.addArgument("no_mask_interpolation", "", mitkCommandLineParser::Bool, "Don't interpolate masks:", "don't interpolate mask image values"); parser.addArgument("compress", "", mitkCommandLineParser::Float, "Compress:", "compress output fibers using the given error threshold (in mm)"); parser.addArgument("fix_seed", "", mitkCommandLineParser::Bool, "Fix Random Seed:", "always use the same random numbers"); parser.endGroup(); std::map parsedArgs = parser.parseArguments(argc, argv); if (parsedArgs.size()==0) return EXIT_FAILURE; mitkCommandLineParser::StringContainerType input_files = us::any_cast(parsedArgs["i"]); std::string outFile = us::any_cast(parsedArgs["o"]); - std::string algorithm = us::any_cast(parsedArgs["algorithm"]); + std::string type = us::any_cast(parsedArgs["type"]); std::shared_ptr< mitk::StreamlineTractographyParameters > params = std::make_shared(); + if (parsedArgs.count("probabilistic")) + params->m_Mode = mitk::StreamlineTractographyParameters::MODE::PROBABILISTIC; + else { + params->m_Mode = mitk::StreamlineTractographyParameters::MODE::DETERMINISTIC; + } + std::string prior_image = ""; if (parsedArgs.count("prior_image")) prior_image = us::any_cast(parsedArgs["prior_image"]); if (parsedArgs.count("prior_weight")) params->m_Weight = us::any_cast(parsedArgs["prior_weight"]); if (parsedArgs.count("fix_seed")) params->m_FixRandomSeed = us::any_cast(parsedArgs["fix_seed"]); params->m_RestrictToPrior = true; if (parsedArgs.count("dont_restrict_to_prior")) params->m_RestrictToPrior = !us::any_cast(parsedArgs["dont_restrict_to_prior"]); params->m_NewDirectionsFromPrior = true; if (parsedArgs.count("no_new_directions_from_prior")) params->m_NewDirectionsFromPrior = !us::any_cast(parsedArgs["no_new_directions_from_prior"]); params->m_SharpenOdfs = false; if (parsedArgs.count("sharpen_odfs")) params->m_SharpenOdfs = us::any_cast(parsedArgs["sharpen_odfs"]); params->m_InterpolateTractographyData = true; if (parsedArgs.count("no_data_interpolation")) params->m_InterpolateTractographyData = !us::any_cast(parsedArgs["no_data_interpolation"]); params->m_InterpolateRoiImages = true; if (parsedArgs.count("no_mask_interpolation")) params->m_InterpolateRoiImages = !us::any_cast(parsedArgs["no_mask_interpolation"]); bool use_sh_features = false; if (parsedArgs.count("use_sh_features")) use_sh_features = us::any_cast(parsedArgs["use_sh_features"]); params->m_StopVotes = false; if (parsedArgs.count("use_stop_votes")) params->m_StopVotes = us::any_cast(parsedArgs["use_stop_votes"]); params->m_OnlyForwardSamples = false; if (parsedArgs.count("use_only_forward_samples")) params->m_OnlyForwardSamples = us::any_cast(parsedArgs["use_only_forward_samples"]); params->m_FlipX = false; if (parsedArgs.count("flip_x")) params->m_FlipX = us::any_cast(parsedArgs["flip_x"]); params->m_FlipY = false; if (parsedArgs.count("flip_y")) params->m_FlipY = us::any_cast(parsedArgs["flip_y"]); params->m_FlipZ = false; if (parsedArgs.count("flip_z")) params->m_FlipZ = us::any_cast(parsedArgs["flip_z"]); bool prior_flip_x = false; if (parsedArgs.count("prior_flip_x")) prior_flip_x = us::any_cast(parsedArgs["prior_flip_x"]); bool prior_flip_y = false; if (parsedArgs.count("prior_flip_y")) prior_flip_y = us::any_cast(parsedArgs["prior_flip_y"]); bool prior_flip_z = false; if (parsedArgs.count("prior_flip_z")) prior_flip_z = us::any_cast(parsedArgs["prior_flip_z"]); params->m_ApplyDirectionMatrix = false; if (parsedArgs.count("apply_image_rotation")) params->m_ApplyDirectionMatrix = us::any_cast(parsedArgs["apply_image_rotation"]); float compress = -1; if (parsedArgs.count("compress")) compress = us::any_cast(parsedArgs["compress"]); params->m_MinTractLength = 20; if (parsedArgs.count("min_tract_length")) params->m_MinTractLength = us::any_cast(parsedArgs["min_tract_length"]); params->SetLoopCheckDeg(-1); if (parsedArgs.count("loop_check")) params->SetLoopCheckDeg(us::any_cast(parsedArgs["loop_check"])); std::string forestFile; if (parsedArgs.count("forest")) forestFile = us::any_cast(parsedArgs["forest"]); std::string maskFile = ""; if (parsedArgs.count("tracking_mask")) maskFile = us::any_cast(parsedArgs["tracking_mask"]); std::string seedFile = ""; if (parsedArgs.count("seed_image")) seedFile = us::any_cast(parsedArgs["seed_image"]); std::string targetFile = ""; if (parsedArgs.count("target_image")) targetFile = us::any_cast(parsedArgs["target_image"]); std::string exclusionFile = ""; if (parsedArgs.count("exclusion_image")) exclusionFile = us::any_cast(parsedArgs["exclusion_image"]); std::string stopFile = ""; if (parsedArgs.count("stop_image")) stopFile = us::any_cast(parsedArgs["stop_image"]); std::string ep_constraint = "NONE"; if (parsedArgs.count("ep_constraint")) ep_constraint = us::any_cast(parsedArgs["ep_constraint"]); params->m_Cutoff = 0.1f; if (parsedArgs.count("cutoff")) params->m_Cutoff = us::any_cast(parsedArgs["cutoff"]); params->m_OdfCutoff = 0.0; if (parsedArgs.count("odf_cutoff")) params->m_OdfCutoff = us::any_cast(parsedArgs["odf_cutoff"]); params->SetStepSizeVox(-1); if (parsedArgs.count("step_size")) params->SetStepSizeVox(us::any_cast(parsedArgs["step_size"])); params->SetSamplingDistance(-1); if (parsedArgs.count("sampling_distance")) params->SetSamplingDistance(us::any_cast(parsedArgs["sampling_distance"])); params->m_NumSamples = 0; if (parsedArgs.count("num_samples")) params->m_NumSamples = static_cast(us::any_cast(parsedArgs["num_samples"])); params->m_SeedsPerVoxel = 1; if (parsedArgs.count("seeds")) params->m_SeedsPerVoxel = us::any_cast(parsedArgs["seeds"]); params->m_TrialsPerSeed = 10; if (parsedArgs.count("trials_per_seed")) params->m_TrialsPerSeed = static_cast(us::any_cast(parsedArgs["trials_per_seed"])); params->m_F = 1; if (parsedArgs.count("tend_f")) params->m_F = us::any_cast(parsedArgs["tend_f"]); params->m_G = 0; if (parsedArgs.count("tend_g")) params->m_G = us::any_cast(parsedArgs["tend_g"]); params->SetAngularThresholdDeg(-1); if (parsedArgs.count("angular_threshold")) params->SetAngularThresholdDeg(us::any_cast(parsedArgs["angular_threshold"])); params->m_MaxNumFibers = -1; if (parsedArgs.count("max_tracts")) params->m_MaxNumFibers = us::any_cast(parsedArgs["max_tracts"]); std::string ext = itksys::SystemTools::GetFilenameExtension(outFile); if (ext != ".fib" && ext != ".trk") { MITK_INFO << "Output file format not supported. Use one of .fib, .trk, .nii, .nii.gz, .nrrd"; return EXIT_FAILURE; } // LOAD DATASETS mitkCommandLineParser::StringContainerType addFiles; if (parsedArgs.count("additional_images")) addFiles = us::any_cast(parsedArgs["additional_images"]); typedef itk::Image ItkFloatImgType; ItkFloatImgType::Pointer mask = nullptr; if (!maskFile.empty()) { MITK_INFO << "loading mask image"; mitk::Image::Pointer img = mitk::IOUtil::Load(maskFile); mask = ItkFloatImgType::New(); mitk::CastToItkImage(img, mask); } ItkFloatImgType::Pointer seed = nullptr; if (!seedFile.empty()) { MITK_INFO << "loading seed ROI image"; mitk::Image::Pointer img = mitk::IOUtil::Load(seedFile); seed = ItkFloatImgType::New(); mitk::CastToItkImage(img, seed); } ItkFloatImgType::Pointer stop = nullptr; if (!stopFile.empty()) { MITK_INFO << "loading stop ROI image"; mitk::Image::Pointer img = mitk::IOUtil::Load(stopFile); stop = ItkFloatImgType::New(); mitk::CastToItkImage(img, stop); } ItkFloatImgType::Pointer target = nullptr; if (!targetFile.empty()) { MITK_INFO << "loading target ROI image"; mitk::Image::Pointer img = mitk::IOUtil::Load(targetFile); target = ItkFloatImgType::New(); mitk::CastToItkImage(img, target); } ItkFloatImgType::Pointer exclusion = nullptr; if (!exclusionFile.empty()) { MITK_INFO << "loading exclusion ROI image"; mitk::Image::Pointer img = mitk::IOUtil::Load(exclusionFile); exclusion = ItkFloatImgType::New(); mitk::CastToItkImage(img, exclusion); } MITK_INFO << "loading additional images"; std::vector< std::vector< ItkFloatImgType::Pointer > > addImages; addImages.push_back(std::vector< ItkFloatImgType::Pointer >()); for (auto file : addFiles) { mitk::Image::Pointer img = mitk::IOUtil::Load(file); ItkFloatImgType::Pointer itkimg = ItkFloatImgType::New(); mitk::CastToItkImage(img, itkimg); addImages.at(0).push_back(itkimg); } // ////////////////////////////////////////////////////////////////// // omp_set_num_threads(1); typedef itk::StreamlineTrackingFilter TrackerType; TrackerType::Pointer tracker = TrackerType::New(); if (!prior_image.empty()) { mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Peak Image"}, {}); mitk::PeakImage::Pointer priorImage = mitk::IOUtil::Load(prior_image, &functor); if (priorImage.IsNull()) { MITK_INFO << "Only peak images are supported as prior at the moment!"; return EXIT_FAILURE; } mitk::TrackingDataHandler* priorhandler = new mitk::TrackingHandlerPeaks(); typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(priorImage); caster->Update(); mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); std::shared_ptr< mitk::StreamlineTractographyParameters > prior_params = std::make_shared< mitk::StreamlineTractographyParameters >(*params); + prior_params->m_FlipX = prior_flip_x; + prior_params->m_FlipY = prior_flip_y; + prior_params->m_FlipZ = prior_flip_z; prior_params->m_Cutoff = 0.0; - prior_params->m_Mode = mitk::StreamlineTractographyParameters::MODE::DETERMINISTIC; dynamic_cast(priorhandler)->SetPeakImage(itkImg); - dynamic_cast(priorhandler)->SetPeakThreshold(0.0); - dynamic_cast(priorhandler)->SetInterpolate(interpolate); - dynamic_cast(priorhandler)->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); - priorhandler->SetParameters(prior_params); tracker->SetTrackingPriorHandler(priorhandler); - tracker->SetTrackingPriorWeight(prior_weight); - tracker->SetTrackingPriorAsMask(restrict_to_prior); - tracker->SetIntroduceDirectionsFromPrior(new_directions_from_prior); } mitk::TrackingDataHandler* handler; - if (algorithm == "DetRF" || algorithm == "ProbRF") + if (type == "RF") { mitk::TractographyForest::Pointer forest = mitk::IOUtil::Load(forestFile); if (forest.IsNull()) mitkThrow() << "Forest file " << forestFile << " could not be read."; mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Diffusion Weighted Images"}, {}); auto input = mitk::IOUtil::Load(input_files.at(0), &functor); if (use_sh_features) { handler = new mitk::TrackingHandlerRandomForest<6,28>(); dynamic_cast*>(handler)->SetForest(forest); dynamic_cast*>(handler)->AddDwi(input); dynamic_cast*>(handler)->SetAdditionalFeatureImages(addImages); } else { handler = new mitk::TrackingHandlerRandomForest<6,100>(); dynamic_cast*>(handler)->SetForest(forest); dynamic_cast*>(handler)->AddDwi(input); dynamic_cast*>(handler)->SetAdditionalFeatureImages(addImages); } - - if (algorithm == "ProbRF") - params->m_Mode = mitk::TrackingDataHandler::MODE::PROBABILISTIC; } - else if (algorithm == "DetPeaks" or algorithm == "ProbPeaks") + else if (type == "Peaks") { handler = new mitk::TrackingHandlerPeaks(); MITK_INFO << "loading input peak image"; mitk::Image::Pointer mitkImage = mitk::IOUtil::Load(input_files.at(0)); mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = mitk::convert::GetItkPeakFromPeakImage(mitkImage); - if (algorithm == "ProbPeaks") - handler->SetMode(mitk::TrackingDataHandler::MODE::PROBABILISTIC); - else - handler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); - dynamic_cast(handler)->SetPeakImage(itkImg); } - else if (algorithm == "DetTensor") + else if (type == "Tensor" && params->m_Mode == mitk::StreamlineTractographyParameters::MODE::DETERMINISTIC) { handler = new mitk::TrackingHandlerTensor(); MITK_INFO << "loading input tensor images"; std::vector< mitk::Image::Pointer > input_images; for (unsigned int i=0; i(input_files.at(i)); mitk::TensorImage::ItkTensorImageType::Pointer itkImg = mitk::convert::GetItkTensorFromTensorImage(mitkImage); dynamic_cast(handler)->AddTensorImage(itkImg.GetPointer()); } if (addImages.at(0).size()>0) dynamic_cast(handler)->SetFaImage(addImages.at(0).at(0)); } - else if (algorithm == "DetODF" || algorithm == "ProbODF" || algorithm == "ProbTensor") + else if (type == "ODF" || (type == "Tensor" && params->m_Mode == mitk::StreamlineTractographyParameters::MODE::PROBABILISTIC)) { handler = new mitk::TrackingHandlerOdf(); mitk::OdfImage::ItkOdfImageType::Pointer itkImg = nullptr; - if (algorithm == "ProbTensor") + if (type == "Tensor") { MITK_INFO << "Converting Tensor to ODF image"; auto input = mitk::IOUtil::Load(input_files.at(0)); itkImg = mitk::convert::GetItkOdfFromTensorImage(input); dynamic_cast(handler)->SetIsOdfFromTensor(true); } else { mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"SH Image", "ODF Image"}, {}); auto input = mitk::IOUtil::Load(input_files.at(0), &functor)[0]; if (dynamic_cast(input.GetPointer())) { MITK_INFO << "Converting SH to ODF image"; mitk::Image::Pointer mitkImg = dynamic_cast(input.GetPointer()); itkImg = mitk::convert::GetItkOdfFromShImage(mitkImg); } else if (dynamic_cast(input.GetPointer())) { mitk::Image::Pointer mitkImg = dynamic_cast(input.GetPointer()); itkImg = mitk::convert::GetItkOdfFromOdfImage(mitkImg); } else mitkThrow() << ""; } dynamic_cast(handler)->SetOdfImage(itkImg); - if (algorithm == "ProbODF" || algorithm == "ProbTensor") - params->m_Mode = mitk::TrackingDataHandler::MODE::PROBABILISTIC; - if (addImages.at(0).size()>0) dynamic_cast(handler)->SetGfaImage(addImages.at(0).at(0)); } else { - MITK_INFO << "Unknown tractography algorithm (" + algorithm+"). Known types are Peaks, DetTensor, ProbTensor, DetODF, ProbODF, DetRF, ProbRF."; + MITK_INFO << "Unknown tractography algorithm (" + type+"). Known types are Peaks, DetTensor, ProbTensor, DetODF, ProbODF, DetRF, ProbRF."; return EXIT_FAILURE; } if (ep_constraint=="NONE") params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::NONE; else if (ep_constraint=="EPS_IN_TARGET") params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET; else if (ep_constraint=="EPS_IN_TARGET_LABELDIFF") params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET_LABELDIFF; else if (ep_constraint=="EPS_IN_SEED_AND_TARGET") params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET; else if (ep_constraint=="MIN_ONE_EP_IN_TARGET") params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::MIN_ONE_EP_IN_TARGET; else if (ep_constraint=="ONE_EP_IN_TARGET") params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::ONE_EP_IN_TARGET; else if (ep_constraint=="NO_EP_IN_TARGET") params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::NO_EP_IN_TARGET; - MITK_INFO << "Tractography algorithm: " << algorithm; + MITK_INFO << "Tractography algorithm: " << type; tracker->SetMaskImage(mask); tracker->SetSeedImage(seed); tracker->SetStoppingRegions(stop); tracker->SetTargetRegions(target); tracker->SetExclusionRegions(exclusion); tracker->SetTrackingHandler(handler); if (ext != ".fib" && ext != ".trk") params->m_OutputProbMap = true; tracker->SetParameters(params); tracker->Update(); if (ext == ".fib" || ext == ".trk") { vtkSmartPointer< vtkPolyData > poly = tracker->GetFiberPolyData(); mitk::FiberBundle::Pointer outFib = mitk::FiberBundle::New(poly); if (compress > 0) outFib->Compress(compress); mitk::IOUtil::Save(outFib, outFile); } else { TrackerType::ItkDoubleImgType::Pointer outImg = tracker->GetOutputProbabilityMap(); mitk::Image::Pointer img = mitk::Image::New(); img->InitializeByItk(outImg.GetPointer()); img->SetVolume(outImg->GetBufferPointer()); if (ext != ".nii" && ext != ".nii.gz" && ext != ".nrrd") outFile += ".nii.gz"; mitk::IOUtil::Save(img, outFile); } delete handler; return EXIT_SUCCESS; } diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingDataHandler.h b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingDataHandler.h index b2ff010f48..d4a209ef02 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingDataHandler.h +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingDataHandler.h @@ -1,105 +1,122 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef _TrackingDataHandler #define _TrackingDataHandler #include #include #include #include #include #include #include #include #include #include #include namespace mitk { /** * \brief Abstract class for tracking handler. A tracking handler deals with determining the next progression direction of a streamline fiber. There are different handlers for tensor images, peak images, ... */ class MITKFIBERTRACKING_EXPORT TrackingDataHandler { public: TrackingDataHandler(); virtual ~TrackingDataHandler(){} typedef itk::Statistics::MersenneTwisterRandomVariateGenerator ItkRngType; typedef boost::mt19937 BoostRngType; typedef itk::Image ItkUcharImgType; typedef itk::Image ItkShortImgType; typedef itk::Image ItkFloatImgType; typedef itk::Image ItkDoubleImgType; typedef vnl_vector_fixed< float, 3 > TrackingDirectionType; typedef mitk::StreamlineTractographyParameters::MODE MODE; virtual TrackingDirectionType ProposeDirection(const itk::Point& pos, std::deque< TrackingDirectionType >& olddirs, itk::Index<3>& oldIndex) = 0; ///< predicts next progression direction at the given position virtual void InitForTracking() = 0; virtual itk::Vector GetSpacing() = 0; virtual itk::Point GetOrigin() = 0; virtual itk::Matrix GetDirection() = 0; virtual itk::ImageRegion<3> GetLargestPossibleRegion() = 0; virtual bool WorldToIndex(itk::Point& pos, itk::Index<3>& index) = 0; void SetParameters(std::shared_ptr< mitk::StreamlineTractographyParameters > parameters) { m_Parameters = parameters; if (m_Parameters->m_FixRandomSeed) { m_Rng.seed(0); std::srand(0); m_RngItk->SetSeed(0); } else { m_Rng.seed(); m_RngItk->SetSeed(); std::srand(std::time(nullptr)); } } double GetRandDouble(const double & a, const double & b) { return m_RngItk->GetUniformVariate(a, b); } protected: + void CalculateMinVoxelSize() + { + itk::Vector< double, 3 > imageSpacing = this->GetSpacing(); + float minVoxelSize = 0; + if(imageSpacing[0](imageSpacing[0]); + else if (imageSpacing[1] < imageSpacing[2]) + minVoxelSize = static_cast(imageSpacing[1]); + else + minVoxelSize = static_cast(imageSpacing[2]); + + if (m_Parameters==nullptr) + mitkThrow() << "No tractography parameter opbect set!"; + + m_Parameters->SetMinVoxelSize(minVoxelSize); + } + BoostRngType m_Rng; ItkRngType::Pointer m_RngItk; bool m_NeedsDataInit; std::shared_ptr< mitk::StreamlineTractographyParameters > m_Parameters; void DataModified() { m_NeedsDataInit = true; } vnl_matrix_fixed m_FloatImageRotation; }; } #endif diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerOdf.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerOdf.cpp index d89a5144b4..ad96ad41cc 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerOdf.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerOdf.cpp @@ -1,306 +1,307 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkTrackingHandlerOdf.h" #include #include #include #include #include namespace mitk { TrackingHandlerOdf::TrackingHandlerOdf() : m_NumProbSamples(1) , m_OdfFromTensor(false) { m_GfaInterpolator = itk::LinearInterpolateImageFunction< itk::Image< float, 3 >, float >::New(); m_OdfInterpolator = itk::LinearInterpolateImageFunction< itk::Image< ItkOdfImageType::PixelType, 3 >, float >::New(); } TrackingHandlerOdf::~TrackingHandlerOdf() { } bool TrackingHandlerOdf::WorldToIndex(itk::Point& pos, itk::Index<3>& index) { m_OdfImage->TransformPhysicalPointToIndex(pos, index); return m_OdfImage->GetLargestPossibleRegion().IsInside(index); } void TrackingHandlerOdf::InitForTracking() { MITK_INFO << "Initializing ODF tracker."; if (m_NeedsDataInit) { m_OdfHemisphereIndices.clear(); itk::OrientationDistributionFunction< float, ODF_SAMPLING_SIZE > odf; vnl_vector_fixed ref; ref.fill(0); ref[0]=1; for (int i=0; i0) m_OdfHemisphereIndices.push_back(i); m_OdfFloatDirs.set_size(m_OdfHemisphereIndices.size(), 3); auto double_dir = m_OdfImage->GetDirection().GetVnlMatrix(); for (int r=0; r<3; r++) for (int c=0; c<3; c++) { m_FloatImageRotation[r][c] = double_dir[r][c]; } for (unsigned int i=0; i GfaFilterType; GfaFilterType::Pointer gfaFilter = GfaFilterType::New(); gfaFilter->SetInput(m_OdfImage); gfaFilter->SetComputationMethod(GfaFilterType::GFA_STANDARD); gfaFilter->Update(); m_GfaImage = gfaFilter->GetOutput(); } + this->CalculateMinVoxelSize(); m_NeedsDataInit = false; } if (m_OdfFromTensor) { m_Parameters->m_OdfCutoff = 0; m_Parameters->m_SharpenOdfs = false; } m_GfaInterpolator->SetInputImage(m_GfaImage); m_OdfInterpolator->SetInputImage(m_OdfImage); std::cout << "TrackingHandlerOdf - GFA threshold: " << m_Parameters->m_Cutoff << std::endl; std::cout << "TrackingHandlerOdf - ODF threshold: " << m_Parameters->m_OdfCutoff << std::endl; if (m_Parameters->m_SharpenOdfs) std::cout << "TrackingHandlerOdf - Sharpening ODfs" << std::endl; } int TrackingHandlerOdf::SampleOdf(vnl_vector< float >& probs, vnl_vector< float >& angles) { boost::random::discrete_distribution dist(probs.begin(), probs.end()); int sampled_idx = 0; int max_sample_idx = -1; float max_prob = 0; int trials = 0; for (int i=0; i> sampler(m_Rng, dist); sampled_idx = sampler(); } if (probs[sampled_idx]>max_prob && probs[sampled_idx]>m_Parameters->m_OdfCutoff && fabs(angles[sampled_idx])>=m_Parameters->GetAngularThreshold()) { max_prob = probs[sampled_idx]; max_sample_idx = sampled_idx; } else if ( (probs[sampled_idx]<=m_Parameters->m_OdfCutoff || fabs(angles[sampled_idx])GetAngularThreshold()) && trials<50) // we allow 50 trials to exceed the ODF threshold i--; } return max_sample_idx; } void TrackingHandlerOdf::SetIsOdfFromTensor(bool OdfFromTensor) { m_OdfFromTensor = OdfFromTensor; } bool TrackingHandlerOdf::GetIsOdfFromTensor() const { return m_OdfFromTensor; } vnl_vector_fixed TrackingHandlerOdf::ProposeDirection(const itk::Point& pos, std::deque >& olddirs, itk::Index<3>& oldIndex) { vnl_vector_fixed output_direction; output_direction.fill(0); itk::Index<3> idx; m_OdfImage->TransformPhysicalPointToIndex(pos, idx); if ( !m_OdfImage->GetLargestPossibleRegion().IsInside(idx) ) return output_direction; // check GFA threshold for termination float gfa = mitk::imv::GetImageValue(pos, m_Parameters->m_InterpolateTractographyData, m_GfaInterpolator); if (gfam_Cutoff) return output_direction; vnl_vector_fixed last_dir; if (!olddirs.empty()) last_dir = olddirs.back(); if (!m_Parameters->m_InterpolateTractographyData && oldIndex==idx) return last_dir; ItkOdfImageType::PixelType odf_values = mitk::imv::GetImageValue(pos, m_Parameters->m_InterpolateTractographyData, m_OdfInterpolator); vnl_vector< float > probs; probs.set_size(m_OdfHemisphereIndices.size()); vnl_vector< float > angles; angles.set_size(m_OdfHemisphereIndices.size()); angles.fill(1.0); // Find ODF maximum and remove <0 values float max_odf_val = 0; float min_odf_val = 999; int max_idx_d = -1; int c = 0; for (int i : m_OdfHemisphereIndices) { if (odf_values[i]<0) odf_values[i] = 0; if (odf_values[i]>max_odf_val) { max_odf_val = odf_values[i]; max_idx_d = c; } if (odf_values[i]m_SharpenOdfs) { // sharpen ODF probs -= min_odf_val; probs /= (max_odf_val-min_odf_val); for (unsigned int i=0; i0) { probs /= odf_sum; max_odf_val /= odf_sum; } } // no previous direction if (max_odf_val>m_Parameters->m_OdfCutoff && (olddirs.empty() || last_dir.magnitude()<=0.5)) { if (m_Parameters->m_Mode==MODE::DETERMINISTIC) // return maximum peak { output_direction = m_OdfFloatDirs.get_row(max_idx_d); return output_direction * max_odf_val; } else if (m_Parameters->m_Mode==MODE::PROBABILISTIC) // sample from complete ODF { int max_sample_idx = SampleOdf(probs, angles); if (max_sample_idx>=0) output_direction = m_OdfFloatDirs.get_row(max_sample_idx) * probs[max_sample_idx]; return output_direction; } } else if (max_odf_val<=m_Parameters->m_OdfCutoff) // return (0,0,0) { return output_direction; } // correct previous direction if (m_Parameters->m_FlipX) last_dir[0] *= -1; if (m_Parameters->m_FlipY) last_dir[1] *= -1; if (m_Parameters->m_FlipZ) last_dir[2] *= -1; // calculate angles between previous direction and ODF directions angles = m_OdfFloatDirs*last_dir; float probs_sum = 0; float max_prob = 0; for (unsigned int i=0; im_Mode==MODE::DETERMINISTIC && odf_val>max_prob && odf_val>m_Parameters->m_OdfCutoff) { // use maximum peak of the ODF weighted with the directional prior max_prob = odf_val; vnl_vector_fixed d = m_OdfFloatDirs.get_row(i); if (angle<0) d *= -1; output_direction = odf_val*d; } else if (m_Parameters->m_Mode==MODE::PROBABILISTIC) { // update ODF probabilties with the ODF values pow(abs_angle, m_DirPriorPower) probs[i] = odf_val; probs_sum += probs[i]; } } // do probabilistic sampling if (m_Parameters->m_Mode==MODE::PROBABILISTIC && probs_sum>0.0001) { int max_sample_idx = SampleOdf(probs, angles); if (max_sample_idx>=0) { output_direction = m_OdfFloatDirs.get_row(max_sample_idx); if (angles[max_sample_idx]<0) output_direction *= -1; output_direction *= probs[max_sample_idx]; } } // check hard angular threshold float mag = output_direction.magnitude(); if (mag>=0.0001) { output_direction.normalize(); float a = dot_product(output_direction, last_dir); if (aGetAngularThreshold()) output_direction.fill(0); } else output_direction.fill(0); if (m_Parameters->m_FlipX) output_direction[0] *= -1; if (m_Parameters->m_FlipY) output_direction[1] *= -1; if (m_Parameters->m_FlipZ) output_direction[2] *= -1; if (m_Parameters->m_ApplyDirectionMatrix) output_direction = m_FloatImageRotation*output_direction; return output_direction; } void TrackingHandlerOdf::SetNumProbSamples(int NumProbSamples) { m_NumProbSamples = NumProbSamples; } } diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerPeaks.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerPeaks.cpp index 623aca610d..53f925d0f7 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerPeaks.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerPeaks.cpp @@ -1,302 +1,304 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkTrackingHandlerPeaks.h" namespace mitk { TrackingHandlerPeaks::TrackingHandlerPeaks() { } TrackingHandlerPeaks::~TrackingHandlerPeaks() { } bool TrackingHandlerPeaks::WorldToIndex(itk::Point& pos, itk::Index<3>& index) { m_DummyImage->TransformPhysicalPointToIndex(pos, index); return m_DummyImage->GetLargestPossibleRegion().IsInside(index); } void TrackingHandlerPeaks::InitForTracking() { MITK_INFO << "Initializing peak tracker."; if (m_NeedsDataInit) { itk::Vector spacing4 = m_PeakImage->GetSpacing(); itk::Point origin4 = m_PeakImage->GetOrigin(); itk::Matrix direction4 = m_PeakImage->GetDirection(); itk::ImageRegion<4> imageRegion4 = m_PeakImage->GetLargestPossibleRegion(); spacing3[0] = spacing4[0]; spacing3[1] = spacing4[1]; spacing3[2] = spacing4[2]; origin3[0] = origin4[0]; origin3[1] = origin4[1]; origin3[2] = origin4[2]; for (int r=0; r<3; r++) for (int c=0; c<3; c++) { direction3[r][c] = direction4[r][c]; m_FloatImageRotation[r][c] = direction4[r][c]; } imageRegion3.SetSize(0, imageRegion4.GetSize()[0]); imageRegion3.SetSize(1, imageRegion4.GetSize()[1]); imageRegion3.SetSize(2, imageRegion4.GetSize()[2]); m_DummyImage = ItkUcharImgType::New(); m_DummyImage->SetSpacing( spacing3 ); m_DummyImage->SetOrigin( origin3 ); m_DummyImage->SetDirection( direction3 ); m_DummyImage->SetRegions( imageRegion3 ); m_DummyImage->Allocate(); m_DummyImage->FillBuffer(0.0); m_NumDirs = imageRegion4.GetSize(3)/3; + + this->CalculateMinVoxelSize(); m_NeedsDataInit = false; } std::cout << "TrackingHandlerPeaks - Peak threshold: " << m_Parameters->m_Cutoff << std::endl; } vnl_vector_fixed TrackingHandlerPeaks::GetMatchingDirection(itk::Index<3> idx3, vnl_vector_fixed& oldDir) { vnl_vector_fixed out_dir; out_dir.fill(0); float angle = 0; float mag = oldDir.magnitude(); if (magm_FixRandomSeed) { // try m_NumDirs times to get a non-zero random direction for (int j=0; jGetIntegerVariate(m_NumDirs-1); out_dir = GetDirection(idx3, i); if (out_dir.magnitude()>mitk::eps) { oldDir[0] = out_dir[0]; oldDir[1] = out_dir[1]; oldDir[2] = out_dir[2]; found = true; break; } } } if (!found) { // if you didn't find a non-zero random direction, take first non-zero direction you find for (int i=0; imitk::eps) { oldDir[0] = out_dir[0]; oldDir[1] = out_dir[1]; oldDir[2] = out_dir[2]; break; } } } } else { for (int i=0; i dir = GetDirection(idx3, i); mag = dir.magnitude(); if (mag>mitk::eps) dir.normalize(); float a = dot_product(dir, oldDir); if (fabs(a)>angle) { angle = fabs(a); if (a<0) out_dir = -dir; else out_dir = dir; out_dir *= mag; out_dir *= angle; // shrink contribution of direction if is less parallel to previous direction } } } return out_dir; } vnl_vector_fixed TrackingHandlerPeaks::GetDirection(itk::Index<3> idx3, int dirIdx) { vnl_vector_fixed dir; dir.fill(0.0); if ( !m_DummyImage->GetLargestPossibleRegion().IsInside(idx3) ) return dir; PeakImgType::IndexType idx4; idx4.SetElement(0,idx3[0]); idx4.SetElement(1,idx3[1]); idx4.SetElement(2,idx3[2]); for (int k=0; k<3; k++) { idx4.SetElement(3, dirIdx*3 + k); dir[k] = m_PeakImage->GetPixel(idx4); } if (m_Parameters->m_FlipX) dir[0] *= -1; if (m_Parameters->m_FlipY) dir[1] *= -1; if (m_Parameters->m_FlipZ) dir[2] *= -1; if (m_Parameters->m_ApplyDirectionMatrix) dir = m_FloatImageRotation*dir; return dir; } vnl_vector_fixed TrackingHandlerPeaks::GetDirection(itk::Point itkP, bool interpolate, vnl_vector_fixed oldDir){ // transform physical point to index coordinates itk::Index<3> idx3; itk::ContinuousIndex< float, 3> cIdx; m_DummyImage->TransformPhysicalPointToIndex(itkP, idx3); m_DummyImage->TransformPhysicalPointToContinuousIndex(itkP, cIdx); vnl_vector_fixed dir; dir.fill(0.0); if ( !m_DummyImage->GetLargestPossibleRegion().IsInside(idx3) ) return dir; if (interpolate) { float frac_x = cIdx[0] - idx3[0]; float frac_y = cIdx[1] - idx3[1]; float frac_z = cIdx[2] - idx3[2]; if (frac_x<0) { idx3[0] -= 1; frac_x += 1; } if (frac_y<0) { idx3[1] -= 1; frac_y += 1; } if (frac_z<0) { idx3[2] -= 1; frac_z += 1; } frac_x = 1-frac_x; frac_y = 1-frac_y; frac_z = 1-frac_z; // int coordinates inside image? if (idx3[0] >= 0 && idx3[0] < static_cast(m_DummyImage->GetLargestPossibleRegion().GetSize(0) - 1) && idx3[1] >= 0 && idx3[1] < static_cast(m_DummyImage->GetLargestPossibleRegion().GetSize(1) - 1) && idx3[2] >= 0 && idx3[2] < static_cast(m_DummyImage->GetLargestPossibleRegion().GetSize(2) - 1)) { // trilinear interpolation vnl_vector_fixed interpWeights; interpWeights[0] = ( frac_x)*( frac_y)*( frac_z); interpWeights[1] = (1-frac_x)*( frac_y)*( frac_z); interpWeights[2] = ( frac_x)*(1-frac_y)*( frac_z); interpWeights[3] = ( frac_x)*( frac_y)*(1-frac_z); interpWeights[4] = (1-frac_x)*(1-frac_y)*( frac_z); interpWeights[5] = ( frac_x)*(1-frac_y)*(1-frac_z); interpWeights[6] = (1-frac_x)*( frac_y)*(1-frac_z); interpWeights[7] = (1-frac_x)*(1-frac_y)*(1-frac_z); dir = GetMatchingDirection(idx3, oldDir) * interpWeights[0]; itk::Index<3> tmpIdx = idx3; tmpIdx[0]++; dir += GetMatchingDirection(tmpIdx, oldDir) * interpWeights[1]; tmpIdx = idx3; tmpIdx[1]++; dir += GetMatchingDirection(tmpIdx, oldDir) * interpWeights[2]; tmpIdx = idx3; tmpIdx[2]++; dir += GetMatchingDirection(tmpIdx, oldDir) * interpWeights[3]; tmpIdx = idx3; tmpIdx[0]++; tmpIdx[1]++; dir += GetMatchingDirection(tmpIdx, oldDir) * interpWeights[4]; tmpIdx = idx3; tmpIdx[1]++; tmpIdx[2]++; dir += GetMatchingDirection(tmpIdx, oldDir) * interpWeights[5]; tmpIdx = idx3; tmpIdx[2]++; tmpIdx[0]++; dir += GetMatchingDirection(tmpIdx, oldDir) * interpWeights[6]; tmpIdx = idx3; tmpIdx[0]++; tmpIdx[1]++; tmpIdx[2]++; dir += GetMatchingDirection(tmpIdx, oldDir) * interpWeights[7]; } } else dir = GetMatchingDirection(idx3, oldDir); return dir; } vnl_vector_fixed TrackingHandlerPeaks::ProposeDirection(const itk::Point& pos, std::deque >& olddirs, itk::Index<3>& oldIndex) { // CHECK: wann wird wo normalisiert vnl_vector_fixed output_direction; output_direction.fill(0); itk::Index<3> index; m_DummyImage->TransformPhysicalPointToIndex(pos, index); vnl_vector_fixed oldDir; oldDir.fill(0.0); if (!olddirs.empty()) oldDir = olddirs.back(); float old_mag = oldDir.magnitude(); if (!m_Parameters->m_InterpolateTractographyData && oldIndex==index) return oldDir; output_direction = GetDirection(pos, m_Parameters->m_InterpolateTractographyData, oldDir); float mag = output_direction.magnitude(); if (mag>=m_Parameters->m_Cutoff) { - if (m_Mode == MODE::PROBABILISTIC) + if (m_Parameters->m_Mode == MODE::PROBABILISTIC) { output_direction[0] += this->m_RngItk->GetNormalVariate(0, fabs(output_direction[0])*0.01); output_direction[1] += this->m_RngItk->GetNormalVariate(0, fabs(output_direction[1])*0.01); output_direction[2] += this->m_RngItk->GetNormalVariate(0, fabs(output_direction[2])*0.01); mag = output_direction.magnitude(); } output_direction.normalize(); float a = 1; if (old_mag>0.5) a = dot_product(output_direction, oldDir); if (a>=m_Parameters->GetAngularThreshold()) output_direction *= mag; else output_direction.fill(0); } else output_direction.fill(0); return output_direction; } } diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerRandomForest.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerRandomForest.cpp index b6be79a25c..45bed795e7 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerRandomForest.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerRandomForest.cpp @@ -1,908 +1,909 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef _TrackingForestHandler_cpp #define _TrackingForestHandler_cpp #include "mitkTrackingHandlerRandomForest.h" #include #include namespace mitk { template< int ShOrder, int NumberOfSignalFeatures > TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::TrackingHandlerRandomForest() : m_WmSampleDistance(-1) , m_NumTrees(30) , m_MaxTreeDepth(25) , m_SampleFraction(1.0) , m_NumberOfSamples(0) , m_GmSamplesPerVoxel(-1) , m_BidirectionalFiberSampling(false) , m_ZeroDirWmFeatures(true) , m_MaxNumWmSamples(-1) { vnl_vector_fixed ref; ref.fill(0); ref[0]=1; itk::OrientationDistributionFunction< float, 200 > odf; m_DirectionContainer.clear(); for (unsigned int i = 0; i odf_dir; odf_dir[0] = odf.GetDirection(i)[0]; odf_dir[1] = odf.GetDirection(i)[1]; odf_dir[2] = odf.GetDirection(i)[2]; if (dot_product(ref, odf_dir)>0) // only used directions on one hemisphere m_DirectionContainer.push_back(odf_dir); // store indices for later mapping the classifier output to the actual direction } m_OdfFloatDirs.set_size(m_DirectionContainer.size(), 3); for (unsigned int i=0; i TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::~TrackingHandlerRandomForest() { } template< int ShOrder, int NumberOfSignalFeatures > bool TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::WorldToIndex(itk::Point& pos, itk::Index<3>& index) { m_DwiFeatureImages.at(0)->TransformPhysicalPointToIndex(pos, index); return m_DwiFeatureImages.at(0)->GetLargestPossibleRegion().IsInside(index); } template< int ShOrder, int NumberOfSignalFeatures > void TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::InputDataValidForTracking() { if (m_InputDwis.empty()) mitkThrow() << "No diffusion-weighted images set!"; if (!IsForestValid()) mitkThrow() << "No or invalid random forest detected!"; } template< int ShOrder, int NumberOfSignalFeatures> template typename std::enable_if< NumberOfSignalFeatures <=99, T >::type TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::InitDwiImageFeatures(mitk::Image::Pointer mitk_dwi) { MITK_INFO << "Calculating spherical harmonics features"; typedef itk::AnalyticalDiffusionQballReconstructionImageFilter InterpolationFilterType; typename InterpolationFilterType::Pointer filter = InterpolationFilterType::New(); filter->SetBValue(mitk::DiffusionPropertyHelper::GetReferenceBValue(mitk_dwi)); filter->SetGradientImage( mitk::DiffusionPropertyHelper::GetOriginalGradientContainer(mitk_dwi), mitk::DiffusionPropertyHelper::GetItkVectorImage(mitk_dwi) ); filter->SetLambda(0.006); filter->SetNormalizationMethod(InterpolationFilterType::QBAR_RAW_SIGNAL); filter->Update(); m_DwiFeatureImages.push_back(filter->GetCoefficientImage()); return true; } template< int ShOrder, int NumberOfSignalFeatures> template typename std::enable_if< NumberOfSignalFeatures >=100, T >::type TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::InitDwiImageFeatures(mitk::Image::Pointer mitk_dwi) { MITK_INFO << "Interpolating raw dwi signal features"; typedef itk::AnalyticalDiffusionQballReconstructionImageFilter InterpolationFilterType; typename InterpolationFilterType::Pointer filter = InterpolationFilterType::New(); filter->SetBValue(mitk::DiffusionPropertyHelper::GetReferenceBValue(mitk_dwi)); filter->SetGradientImage( mitk::DiffusionPropertyHelper::GetOriginalGradientContainer(mitk_dwi), mitk::DiffusionPropertyHelper::GetItkVectorImage(mitk_dwi) ); filter->SetLambda(0.006); filter->SetNormalizationMethod(InterpolationFilterType::QBAR_RAW_SIGNAL); filter->Update(); typename DwiFeatureImageType::Pointer dwiFeatureImage = DwiFeatureImageType::New(); dwiFeatureImage->SetSpacing(filter->GetOutput()->GetSpacing()); dwiFeatureImage->SetOrigin(filter->GetOutput()->GetOrigin()); dwiFeatureImage->SetDirection(filter->GetOutput()->GetDirection()); dwiFeatureImage->SetLargestPossibleRegion(filter->GetOutput()->GetLargestPossibleRegion()); dwiFeatureImage->SetBufferedRegion(filter->GetOutput()->GetLargestPossibleRegion()); dwiFeatureImage->SetRequestedRegion(filter->GetOutput()->GetLargestPossibleRegion()); dwiFeatureImage->Allocate(); // get signal values and store them in the feature image vnl_vector_fixed ref; ref.fill(0); ref[0]=1; itk::OrientationDistributionFunction< float, 2*NumberOfSignalFeatures > odf; itk::ImageRegionIterator< typename InterpolationFilterType::OutputImageType > it(filter->GetOutput(), filter->GetOutput()->GetLargestPossibleRegion()); while(!it.IsAtEnd()) { typename DwiFeatureImageType::PixelType pix; int f = 0; for (unsigned int i = 0; i0) // only used directions on one hemisphere { pix[f] = it.Get()[i]; f++; } } dwiFeatureImage->SetPixel(it.GetIndex(), pix); ++it; } m_DwiFeatureImages.push_back(dwiFeatureImage); return true; } template< int ShOrder, int NumberOfSignalFeatures > void TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::InitForTracking() { MITK_INFO << "Initializing random forest tracker."; if (m_NeedsDataInit) { InputDataValidForTracking(); m_DwiFeatureImages.clear(); InitDwiImageFeatures<>(m_InputDwis.at(0)); // initialize interpolators m_DwiFeatureImageInterpolator = DwiFeatureImageInterpolatorType::New(); m_DwiFeatureImageInterpolator->SetInputImage(m_DwiFeatureImages.at(0)); m_AdditionalFeatureImageInterpolators.clear(); for (auto afi_vec : m_AdditionalFeatureImages) { std::vector< FloatImageInterpolatorType::Pointer > v; for (auto img : afi_vec) { FloatImageInterpolatorType::Pointer interp = FloatImageInterpolatorType::New(); interp->SetInputImage(img); v.push_back(interp); } m_AdditionalFeatureImageInterpolators.push_back(v); } auto double_dir = m_DwiFeatureImages.at(0)->GetDirection().GetVnlMatrix(); for (int r=0; r<3; r++) for (int c=0; c<3; c++) { m_FloatImageRotation[r][c] = double_dir[r][c]; } + this->CalculateMinVoxelSize(); m_NeedsDataInit = false; } } template< int ShOrder, int NumberOfSignalFeatures > vnl_vector_fixed TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::ProposeDirection(const itk::Point& pos, std::deque >& olddirs, itk::Index<3>& oldIndex) { vnl_vector_fixed output_direction; output_direction.fill(0); itk::Index<3> idx; m_DwiFeatureImages.at(0)->TransformPhysicalPointToIndex(pos, idx); bool check_last_dir = false; vnl_vector_fixed last_dir; if (!olddirs.empty()) { last_dir = olddirs.back(); if (last_dir.magnitude()>0.5) check_last_dir = true; } if (!m_Parameters->m_InterpolateTractographyData && oldIndex==idx) return last_dir; // store feature pixel values in a vigra data type vigra::MultiArray<2, float> featureData = vigra::MultiArray<2, float>( vigra::Shape2(1,m_Forest->GetNumFeatures()) ); featureData.init(0.0); typename DwiFeatureImageType::PixelType dwiFeaturePixel = mitk::imv::GetImageValue< typename DwiFeatureImageType::PixelType >(pos, m_Parameters->m_InterpolateTractographyData, m_DwiFeatureImageInterpolator); for (unsigned int f=0; f direction_matrix = m_DwiFeatureImages.at(0)->GetDirection().GetVnlMatrix(); vnl_matrix_fixed inverse_direction_matrix = m_DwiFeatureImages.at(0)->GetInverseDirection().GetVnlMatrix(); // append normalized previous direction(s) to feature vector int i = 0; vnl_vector_fixed ref; ref.fill(0); ref[0]=1; for (auto d : olddirs) { vnl_vector_fixed tempD; tempD[0] = d[0]; tempD[1] = d[1]; tempD[2] = d[2]; if (m_Parameters->m_FlipX) tempD[0] *= -1; if (m_Parameters->m_FlipY) tempD[1] *= -1; if (m_Parameters->m_FlipZ) tempD[2] *= -1; tempD = inverse_direction_matrix * tempD; last_dir[0] = tempD[0]; last_dir[1] = tempD[1]; last_dir[2] = tempD[2]; int c = 0; for (int f=NumberOfSignalFeatures+3*i; f0) { int c = 0; for (auto interpolator : m_AdditionalFeatureImageInterpolators.at(0)) { float v = mitk::imv::GetImageValue(pos, false, interpolator); featureData(0,NumberOfSignalFeatures+m_Parameters->m_NumPreviousDirections*3+c) = v; c++; } } // perform classification vigra::MultiArray<2, float> probs(vigra::Shape2(1, m_Forest->GetNumClasses())); m_Forest->PredictProbabilities(featureData, probs); vnl_vector< float > angles = m_OdfFloatDirs*last_dir; vnl_vector< float > probs2; probs2.set_size(m_DirectionContainer.size()); probs2.fill(0.0); // used for probabilistic direction sampling float probs_sum = 0; float pNonFib = 0; // probability that we left the white matter float w = 0; // weight of the predicted direction for (int i=0; iGetNumClasses(); i++) // for each class (number of possible directions + out-of-wm class) { if (probs(0,i)>0) // if probability of respective class is 0, do nothing { // get label of class (does not correspond to the loop variable i) unsigned int classLabel = m_Forest->IndexToClassLabel(i); if (classLabelm_Mode==MODE::PROBABILISTIC) { probs2[classLabel] = probs(0,i); if (check_last_dir) probs2[classLabel] *= abs_angle; probs_sum += probs2[classLabel]; } else if (m_Parameters->m_Mode==MODE::DETERMINISTIC) { vnl_vector_fixed d = m_DirectionContainer.at(classLabel); // get direction vector assiciated with the respective direction index if (check_last_dir) // do we have a previous streamline direction or did we just start? { if (abs_angle>=m_Parameters->GetAngularThreshold()) // is angle between the directions smaller than our hard threshold? { if (angle<0) // make sure we don't walk backwards d *= -1; float w_i = probs(0,i)*abs_angle; output_direction += w_i*d; // weight contribution to output direction with its probability and the angular deviation from the previous direction w += w_i; // increase output weight of the final direction } } else { output_direction += probs(0,i)*d; w += probs(0,i); } } } else pNonFib += probs(0,i); // probability that we are not in the white matter anymore } } if (m_Parameters->m_Mode==MODE::PROBABILISTIC && pNonFib<0.5) { boost::random::discrete_distribution dist(probs2.begin(), probs2.end()); int sampled_idx = 0; for (int i=0; i<50; i++) // we allow 50 trials to exceed m_AngularThreshold { #pragma omp critical { boost::random::variate_generator> sampler(m_Rng, dist); sampled_idx = sampler(); } if ( probs2[sampled_idx]>0.1 && (!check_last_dir || (check_last_dir && fabs(angles[sampled_idx])>=m_Parameters->GetAngularThreshold())) ) break; } output_direction = m_DirectionContainer.at(sampled_idx); w = probs2[sampled_idx]; if (check_last_dir && angles[sampled_idx]<0) // make sure we don't walk backwards output_direction *= -1; } // if we did not find a suitable direction, make sure that we return (0,0,0) if (pNonFib>w && w>0) output_direction.fill(0.0); else { vnl_vector_fixed tempD; tempD[0] = output_direction[0]; tempD[1] = output_direction[1]; tempD[2] = output_direction[2]; tempD = direction_matrix * tempD; output_direction[0] = tempD[0]; output_direction[1] = tempD[1]; output_direction[2] = tempD[2]; if (m_Parameters->m_FlipX) output_direction[0] *= -1; if (m_Parameters->m_FlipY) output_direction[1] *= -1; if (m_Parameters->m_FlipZ) output_direction[2] *= -1; if (m_Parameters->m_ApplyDirectionMatrix) output_direction = m_FloatImageRotation*output_direction; } return output_direction * w; } template< int ShOrder, int NumberOfSignalFeatures > void TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::StartTraining() { m_StartTime = std::chrono::system_clock::now(); InputDataValidForTraining(); InitForTraining(); CalculateTrainingSamples(); MITK_INFO << "Maximum tree depths: " << m_MaxTreeDepth; MITK_INFO << "Sample fraction per tree: " << m_SampleFraction; MITK_INFO << "Number of trees: " << m_NumTrees; DefaultSplitType splitter; splitter.UsePointBasedWeights(true); splitter.SetWeights(m_Weights); splitter.UseRandomSplit(false); splitter.SetPrecision(mitk::eps); splitter.SetMaximumTreeDepth(m_MaxTreeDepth); std::vector< std::shared_ptr< vigra::RandomForest > > trees; int count = 0; #pragma omp parallel for for (int i = 0; i < m_NumTrees; ++i) { std::shared_ptr< vigra::RandomForest > lrf = std::make_shared< vigra::RandomForest >(); lrf->set_options().use_stratification(vigra::RF_NONE); // How the data should be made equal lrf->set_options().sample_with_replacement(true); // if sampled with replacement or not lrf->set_options().samples_per_tree(m_SampleFraction); // Fraction of samples that are used to train a tree lrf->set_options().tree_count(1); // Number of trees that are calculated; lrf->set_options().min_split_node_size(5); // Minimum number of datapoints that must be in a node lrf->ext_param_.max_tree_depth = m_MaxTreeDepth; lrf->learn(m_FeatureData, m_LabelData,vigra::rf::visitors::VisitorBase(),splitter); #pragma omp critical { count++; MITK_INFO << "Tree " << count << " finished training."; trees.push_back(lrf); } } for (int i = 1; i < m_NumTrees; ++i) trees.at(0)->trees_.push_back(trees.at(i)->trees_[0]); std::shared_ptr< vigra::RandomForest > forest = trees.at(0); forest->options_.tree_count_ = m_NumTrees; m_Forest = mitk::TractographyForest::New(forest); MITK_INFO << "Training finsihed"; m_EndTime = std::chrono::system_clock::now(); std::chrono::hours hh = std::chrono::duration_cast(m_EndTime - m_StartTime); std::chrono::minutes mm = std::chrono::duration_cast(m_EndTime - m_StartTime); mm %= 60; MITK_INFO << "Training took " << hh.count() << "h and " << mm.count() << "m"; } template< int ShOrder, int NumberOfSignalFeatures > void TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::InputDataValidForTraining() { if (m_InputDwis.empty()) mitkThrow() << "No diffusion-weighted images set!"; if (m_Tractograms.empty()) mitkThrow() << "No tractograms set!"; if (m_InputDwis.size()!=m_Tractograms.size()) mitkThrow() << "Unequal number of diffusion-weighted images and tractograms detected!"; } template< int ShOrder, int NumberOfSignalFeatures > bool TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::IsForestValid() { int additional_features = 0; if (m_AdditionalFeatureImages.size()>0) additional_features = m_AdditionalFeatureImages.at(0).size(); if (!m_Forest) MITK_INFO << "No forest available!"; else { if (m_Forest->GetNumTrees() <= 0) MITK_ERROR << "Forest contains no trees!"; if ( m_Forest->GetNumFeatures() != static_cast(NumberOfSignalFeatures+3*m_Parameters->m_NumPreviousDirections+additional_features) ) MITK_ERROR << "Wrong number of features in forest: got " << m_Forest->GetNumFeatures() << ", expected " << (NumberOfSignalFeatures+3*m_Parameters->m_NumPreviousDirections+additional_features); } if(m_Forest && m_Forest->GetNumTrees()>0 && m_Forest->GetNumFeatures() == static_cast(NumberOfSignalFeatures+3*m_Parameters->m_NumPreviousDirections+additional_features)) return true; return false; } template< int ShOrder, int NumberOfSignalFeatures > void TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::InitForTraining() { MITK_INFO << "Spherical signal interpolation and sampling ..."; for (unsigned int i=0; i(m_InputDwis.at(i)); if (i>=m_AdditionalFeatureImages.size()) { m_AdditionalFeatureImages.push_back(std::vector< ItkFloatImgType::Pointer >()); } if (i>=m_FiberVolumeModImages.size()) { ItkFloatImgType::Pointer img = ItkFloatImgType::New(); img->SetSpacing( m_DwiFeatureImages.at(i)->GetSpacing() ); img->SetOrigin( m_DwiFeatureImages.at(i)->GetOrigin() ); img->SetDirection( m_DwiFeatureImages.at(i)->GetDirection() ); img->SetLargestPossibleRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); img->SetBufferedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); img->SetRequestedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); img->Allocate(); img->FillBuffer(1); m_FiberVolumeModImages.push_back(img); } if (m_FiberVolumeModImages.at(i)==nullptr) { m_FiberVolumeModImages.at(i) = ItkFloatImgType::New(); m_FiberVolumeModImages.at(i)->SetSpacing( m_DwiFeatureImages.at(i)->GetSpacing() ); m_FiberVolumeModImages.at(i)->SetOrigin( m_DwiFeatureImages.at(i)->GetOrigin() ); m_FiberVolumeModImages.at(i)->SetDirection( m_DwiFeatureImages.at(i)->GetDirection() ); m_FiberVolumeModImages.at(i)->SetLargestPossibleRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); m_FiberVolumeModImages.at(i)->SetBufferedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); m_FiberVolumeModImages.at(i)->SetRequestedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); m_FiberVolumeModImages.at(i)->Allocate(); m_FiberVolumeModImages.at(i)->FillBuffer(1); } if (i>=m_MaskImages.size()) { ItkUcharImgType::Pointer newMask = ItkUcharImgType::New(); newMask->SetSpacing( m_DwiFeatureImages.at(i)->GetSpacing() ); newMask->SetOrigin( m_DwiFeatureImages.at(i)->GetOrigin() ); newMask->SetDirection( m_DwiFeatureImages.at(i)->GetDirection() ); newMask->SetLargestPossibleRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); newMask->SetBufferedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); newMask->SetRequestedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); newMask->Allocate(); newMask->FillBuffer(1); m_MaskImages.push_back(newMask); } if (m_MaskImages.at(i)==nullptr) { m_MaskImages.at(i) = ItkUcharImgType::New(); m_MaskImages.at(i)->SetSpacing( m_DwiFeatureImages.at(i)->GetSpacing() ); m_MaskImages.at(i)->SetOrigin( m_DwiFeatureImages.at(i)->GetOrigin() ); m_MaskImages.at(i)->SetDirection( m_DwiFeatureImages.at(i)->GetDirection() ); m_MaskImages.at(i)->SetLargestPossibleRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); m_MaskImages.at(i)->SetBufferedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); m_MaskImages.at(i)->SetRequestedRegion( m_DwiFeatureImages.at(i)->GetLargestPossibleRegion() ); m_MaskImages.at(i)->Allocate(); m_MaskImages.at(i)->FillBuffer(1); } } // initialize interpolators m_AdditionalFeatureImageInterpolators.clear(); for (auto afi_vec : m_AdditionalFeatureImages) { std::vector< FloatImageInterpolatorType::Pointer > v; for (auto img : afi_vec) { FloatImageInterpolatorType::Pointer interp = FloatImageInterpolatorType::New(); interp->SetInputImage(img); v.push_back(interp); } m_AdditionalFeatureImageInterpolators.push_back(v); } MITK_INFO << "Resampling fibers and calculating number of samples ..."; m_NumberOfSamples = 0; m_SampleUsage.clear(); for (unsigned int t=0; t::Pointer env = itk::TractDensityImageFilter< ItkUcharImgType >::New(); env->SetFiberBundle(m_Tractograms.at(t)); env->SetInputImage(mask); env->SetBinaryOutput(true); env->SetUseImageGeometry(true); env->Update(); wmmask = env->GetOutput(); if (t>=m_WhiteMatterImages.size()) m_WhiteMatterImages.push_back(wmmask); else m_WhiteMatterImages.at(t) = wmmask; } // Calculate white-matter samples if (m_WmSampleDistance<0) { typename DwiFeatureImageType::Pointer image = m_DwiFeatureImages.at(t); float minSpacing = 1; if(image->GetSpacing()[0]GetSpacing()[1] && image->GetSpacing()[0]GetSpacing()[2]) minSpacing = image->GetSpacing()[0]; else if (image->GetSpacing()[1] < image->GetSpacing()[2]) minSpacing = image->GetSpacing()[1]; else minSpacing = image->GetSpacing()[2]; m_WmSampleDistance = minSpacing*0.5; } m_Tractograms.at(t)->ResampleLinear(m_WmSampleDistance); int wmSamples = m_Tractograms.at(t)->GetNumberOfPoints()-2*m_Tractograms.at(t)->GetNumFibers(); if (m_BidirectionalFiberSampling) wmSamples *= 2; if (m_ZeroDirWmFeatures) wmSamples *= (m_Parameters->m_NumPreviousDirections+1); MITK_INFO << "White matter samples available: " << wmSamples; // upper limit for samples if (m_MaxNumWmSamples>0 && wmSamples>m_MaxNumWmSamples) { if ((float)m_MaxNumWmSamples/wmSamples > 0.8) { m_SampleUsage.push_back(std::vector(wmSamples, true)); m_NumberOfSamples += wmSamples; } else { m_SampleUsage.push_back(std::vector(wmSamples, false)); m_NumberOfSamples += m_MaxNumWmSamples; wmSamples = m_MaxNumWmSamples; MITK_INFO << "Limiting white matter samples to: " << m_MaxNumWmSamples; itk::Statistics::MersenneTwisterRandomVariateGenerator::Pointer randgen = itk::Statistics::MersenneTwisterRandomVariateGenerator::New(); randgen->SetSeed(); int c = 0; while (cGetIntegerVariate(m_MaxNumWmSamples-1); if (m_SampleUsage[t][idx]==false) { m_SampleUsage[t][idx]=true; ++c; } } } } else { m_SampleUsage.push_back(std::vector(wmSamples, true)); m_NumberOfSamples += wmSamples; } // calculate gray-matter samples itk::ImageRegionConstIterator it(wmmask, wmmask->GetLargestPossibleRegion()); int OUTOFWM = 0; while(!it.IsAtEnd()) { if (it.Get()==0 && mask->GetPixel(it.GetIndex())>0) OUTOFWM++; ++it; } MITK_INFO << "Non-white matter voxels: " << OUTOFWM; if (m_GmSamplesPerVoxel>0) { m_GmSamples.push_back(m_GmSamplesPerVoxel); m_NumberOfSamples += m_GmSamplesPerVoxel*OUTOFWM; } else if (OUTOFWM>0) { int gm_per_voxel = 0.5+(float)wmSamples/(float)OUTOFWM; if (gm_per_voxel<=0) gm_per_voxel = 1; m_GmSamples.push_back(gm_per_voxel); m_NumberOfSamples += m_GmSamples.back()*OUTOFWM; MITK_INFO << "Non-white matter samples per voxel: " << m_GmSamples.back(); } else { m_GmSamples.push_back(0); } MITK_INFO << "Non-white matter samples: " << m_GmSamples.back()*OUTOFWM; } MITK_INFO << "Number of samples: " << m_NumberOfSamples; } template< int ShOrder, int NumberOfSignalFeatures > void TrackingHandlerRandomForest< ShOrder, NumberOfSignalFeatures >::CalculateTrainingSamples() { vnl_vector_fixed ref; ref.fill(0); ref[0]=1; m_FeatureData.reshape( vigra::Shape2(m_NumberOfSamples, NumberOfSignalFeatures+m_Parameters->m_NumPreviousDirections*3+m_AdditionalFeatureImages.at(0).size()) ); m_LabelData.reshape( vigra::Shape2(m_NumberOfSamples,1) ); m_Weights.reshape( vigra::Shape2(m_NumberOfSamples,1) ); MITK_INFO << "Number of features: " << m_FeatureData.shape(1); itk::Statistics::MersenneTwisterRandomVariateGenerator::Pointer m_RandGen = itk::Statistics::MersenneTwisterRandomVariateGenerator::New(); m_RandGen->SetSeed(); MITK_INFO << "Creating training data ..."; unsigned int sampleCounter = 0; for (unsigned int t=0; tSetInputImage(fiber_folume); typename DwiFeatureImageType::Pointer image = m_DwiFeatureImages.at(t); typename DwiFeatureImageInterpolatorType::Pointer dwi_interp = DwiFeatureImageInterpolatorType::New(); dwi_interp->SetInputImage(image); ItkUcharImgType::Pointer wmMask = m_WhiteMatterImages.at(t); ItkUcharImgType::Pointer mask; if (t it(wmMask, wmMask->GetLargestPossibleRegion()); while(!it.IsAtEnd()) { if (it.Get()==0 && (mask.IsNull() || (mask.IsNotNull() && mask->GetPixel(it.GetIndex())>0))) { typename DwiFeatureImageType::PixelType pix = image->GetPixel(it.GetIndex()); // random directions for (unsigned int i=0; iGetIntegerVariate(m_Parameters->m_NumPreviousDirections); // how many directions should be zero? for (unsigned int i=0; im_NumPreviousDirections; i++) { int c=0; vnl_vector_fixed probe; if (static_cast(i)GetVariate()*2-1; probe[1] = m_RandGen->GetVariate()*2-1; probe[2] = m_RandGen->GetVariate()*2-1; probe.normalize(); if (dot_product(ref, probe)<0) probe *= -1; } for (unsigned int f=NumberOfSignalFeatures+3*i; f itkP; image->TransformIndexToPhysicalPoint(it.GetIndex(), itkP); float v = mitk::imv::GetImageValue(itkP, false, interpolator); m_FeatureData(sampleCounter,NumberOfSignalFeatures+m_Parameters->m_NumPreviousDirections*3+add_feat_c) = v; add_feat_c++; } // label and sample weight m_LabelData(sampleCounter,0) = m_DirectionContainer.size(); m_Weights(sampleCounter,0) = 1.0; sampleCounter++; } } ++it; } unsigned int num_gm_samples = sampleCounter; // white matter samples mitk::FiberBundle::Pointer fib = m_Tractograms.at(t); vtkSmartPointer< vtkPolyData > polyData = fib->GetFiberPolyData(); vnl_vector_fixed zero_dir; zero_dir.fill(0.0); for (unsigned int i=0; iGetNumFibers(); i++) { vtkCell* cell = polyData->GetCell(i); int numPoints = cell->GetNumberOfPoints(); vtkPoints* points = cell->GetPoints(); float fiber_weight = fib->GetFiberWeight(i); for (int n = 0; n <= static_cast(m_Parameters->m_NumPreviousDirections); ++n) { if (!m_ZeroDirWmFeatures) n = m_Parameters->m_NumPreviousDirections; for (bool reverse : {false, true}) { for (int j=1; j itkP1, itkP2; int num_nonzero_dirs = m_Parameters->m_NumPreviousDirections; if (!reverse) num_nonzero_dirs = std::min(n, j); else num_nonzero_dirs = std::min(n, numPoints-j-1); vnl_vector_fixed dir; // zero directions for (unsigned int k=0; km_NumPreviousDirections-num_nonzero_dirs; k++) { dir.fill(0.0); int c = 0; for (unsigned int f=NumberOfSignalFeatures+3*k; fGetPoint(j-n_idx); itkP1[0] = p[0]; itkP1[1] = p[1]; itkP1[2] = p[2]; p = points->GetPoint(j-n_idx+1); itkP2[0] = p[0]; itkP2[1] = p[1]; itkP2[2] = p[2]; } else { p = points->GetPoint(j+n_idx); itkP1[0] = p[0]; itkP1[1] = p[1]; itkP1[2] = p[2]; p = points->GetPoint(j+n_idx-1); itkP2[0] = p[0]; itkP2[1] = p[1]; itkP2[2] = p[2]; } dir[0]=itkP1[0]-itkP2[0]; dir[1]=itkP1[1]-itkP2[1]; dir[2]=itkP1[2]-itkP2[2]; if (dir.magnitude()<0.0001) mitkThrow() << "streamline error!"; dir.normalize(); if (dir[0]!=dir[0] || dir[1]!=dir[1] || dir[2]!=dir[2]) mitkThrow() << "ERROR: NaN direction!"; if (dot_product(ref, dir)<0) dir *= -1; int c = 0; for (unsigned int f=NumberOfSignalFeatures+3*(k+m_Parameters->m_NumPreviousDirections-num_nonzero_dirs); fm_NumPreviousDirections-num_nonzero_dirs); f++) { m_FeatureData(sampleCounter,f) = dir[c]; c++; } } // get target direction double* p = points->GetPoint(j); itkP1[0] = p[0]; itkP1[1] = p[1]; itkP1[2] = p[2]; if (reverse) { p = points->GetPoint(j-1); itkP2[0] = p[0]; itkP2[1] = p[1]; itkP2[2] = p[2]; } else { p = points->GetPoint(j+1); itkP2[0] = p[0]; itkP2[1] = p[1]; itkP2[2] = p[2]; } dir[0]=itkP2[0]-itkP1[0]; dir[1]=itkP2[1]-itkP1[1]; dir[2]=itkP2[2]-itkP1[2]; if (dir.magnitude()<0.0001) mitkThrow() << "streamline error!"; dir.normalize(); if (dir[0]!=dir[0] || dir[1]!=dir[1] || dir[2]!=dir[2]) mitkThrow() << "ERROR: NaN direction!"; if (dot_product(ref, dir)<0) dir *= -1; // image features float volume_mod = mitk::imv::GetImageValue(itkP1, false, volume_interpolator); // diffusion signal features typename DwiFeatureImageType::PixelType pix = mitk::imv::GetImageValue< typename DwiFeatureImageType::PixelType >(itkP1, m_Parameters->m_InterpolateTractographyData, dwi_interp); for (unsigned int f=0; f(itkP1, false, interpolator); add_feat_c++; m_FeatureData(sampleCounter,NumberOfSignalFeatures+2+add_feat_c) = v; } // set label values float angle = 0; float m = dir.magnitude(); if (m>0.0001) { int l = 0; for (auto d : m_DirectionContainer) { float a = fabs(dot_product(dir, d)); if (a>angle) { m_LabelData(sampleCounter,0) = l; m_Weights(sampleCounter,0) = fiber_weight*volume_mod; angle = a; } l++; } } sampleCounter++; } if (!m_BidirectionalFiberSampling) // don't sample fibers backward break; } } } } m_Tractograms.clear(); MITK_INFO << "done"; } } #endif diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerTensor.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerTensor.cpp index e21d5651ac..3eaf937848 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerTensor.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/TrackingHandlers/mitkTrackingHandlerTensor.cpp @@ -1,377 +1,378 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkTrackingHandlerTensor.h" namespace mitk { TrackingHandlerTensor::TrackingHandlerTensor() : m_InterpolateTensors(true) , m_NumberOfInputs(0) { m_FaInterpolator = itk::LinearInterpolateImageFunction< itk::Image< float, 3 >, float >::New(); } TrackingHandlerTensor::~TrackingHandlerTensor() { } void TrackingHandlerTensor::InitForTracking() { MITK_INFO << "Initializing tensor tracker."; if (m_NeedsDataInit) { m_NumberOfInputs = m_TensorImages.size(); for (int i=0; iSetSpacing( m_TensorImages.at(0)->GetSpacing() ); pdImage->SetOrigin( m_TensorImages.at(0)->GetOrigin() ); pdImage->SetDirection( m_TensorImages.at(0)->GetDirection() ); pdImage->SetRegions( m_TensorImages.at(0)->GetLargestPossibleRegion() ); pdImage->Allocate(); m_PdImage.push_back(pdImage); ItkDoubleImgType::Pointer emaxImage = ItkDoubleImgType::New(); emaxImage->SetSpacing( m_TensorImages.at(0)->GetSpacing() ); emaxImage->SetOrigin( m_TensorImages.at(0)->GetOrigin() ); emaxImage->SetDirection( m_TensorImages.at(0)->GetDirection() ); emaxImage->SetRegions( m_TensorImages.at(0)->GetLargestPossibleRegion() ); emaxImage->Allocate(); emaxImage->FillBuffer(1.0); m_EmaxImage.push_back(emaxImage); } bool useUserFaImage = true; if (m_FaImage.IsNull()) { m_FaImage = ItkFloatImgType::New(); m_FaImage->SetSpacing( m_TensorImages.at(0)->GetSpacing() ); m_FaImage->SetOrigin( m_TensorImages.at(0)->GetOrigin() ); m_FaImage->SetDirection( m_TensorImages.at(0)->GetDirection() ); m_FaImage->SetRegions( m_TensorImages.at(0)->GetLargestPossibleRegion() ); m_FaImage->Allocate(); m_FaImage->FillBuffer(0.0); useUserFaImage = false; } typedef itk::DiffusionTensor3D TensorType; for (int x=0; x<(int)m_TensorImages.at(0)->GetLargestPossibleRegion().GetSize()[0]; x++) for (int y=0; y<(int)m_TensorImages.at(0)->GetLargestPossibleRegion().GetSize()[1]; y++) for (int z=0; z<(int)m_TensorImages.at(0)->GetLargestPossibleRegion().GetSize()[2]; z++) { ItkTensorImageType::IndexType index; index[0] = x; index[1] = y; index[2] = z; for (int i=0; i dir; tensor = m_TensorImages.at(i)->GetPixel(index); tensor.ComputeEigenAnalysis(eigenvalues, eigenvectors); dir[0] = eigenvectors(2, 0); dir[1] = eigenvectors(2, 1); dir[2] = eigenvectors(2, 2); if (dir.magnitude()>mitk::eps) dir.normalize(); else dir.fill(0.0); m_PdImage.at(i)->SetPixel(index, dir); if (!useUserFaImage) m_FaImage->SetPixel(index, m_FaImage->GetPixel(index)+tensor.GetFractionalAnisotropy()); m_EmaxImage.at(i)->SetPixel(index, 2/eigenvalues[2]); } if (!useUserFaImage) m_FaImage->SetPixel(index, m_FaImage->GetPixel(index)/m_NumberOfInputs); } auto double_dir = m_TensorImages.at(0)->GetDirection().GetVnlMatrix(); for (int r=0; r<3; r++) for (int c=0; c<3; c++) { m_FloatImageRotation[r][c] = double_dir[r][c]; } + this->CalculateMinVoxelSize(); m_NeedsDataInit = false; } if (m_Parameters->m_F+m_Parameters->m_G>1.0) { float temp = m_Parameters->m_F+m_Parameters->m_G; m_Parameters->m_F /= temp; m_Parameters->m_G /= temp; } m_FaInterpolator->SetInputImage(m_FaImage); std::cout << "TrackingHandlerTensor - FA threshold: " << m_Parameters->m_Cutoff << std::endl; std::cout << "TrackingHandlerTensor - f: " << m_Parameters->m_F << std::endl; std::cout << "TrackingHandlerTensor - g: " << m_Parameters->m_G << std::endl; } vnl_vector_fixed TrackingHandlerTensor::GetMatchingDirection(itk::Index<3> idx, vnl_vector_fixed& oldDir, int& image_num) { vnl_vector_fixed out_dir; out_dir.fill(0); float angle = 0; float mag = oldDir.magnitude(); if (magGetPixel(idx); if (out_dir.magnitude()>0.5) { image_num = i; oldDir[0] = out_dir[0]; oldDir[1] = out_dir[1]; oldDir[2] = out_dir[2]; break; } } } else { for (unsigned int i=0; i dir = m_PdImage.at(i)->GetPixel(idx); float a = dot_product(dir, oldDir); if (fabs(a)>angle) { image_num = i; angle = fabs(a); if (a<0) out_dir = -dir; else out_dir = dir; out_dir *= angle; // shrink contribution of direction if is less parallel to previous direction } } } return out_dir; } bool TrackingHandlerTensor::WorldToIndex(itk::Point& pos, itk::Index<3>& index) { m_TensorImages.at(0)->TransformPhysicalPointToIndex(pos, index); return m_TensorImages.at(0)->GetLargestPossibleRegion().IsInside(index); } vnl_vector_fixed TrackingHandlerTensor::GetDirection(itk::Point itkP, vnl_vector_fixed oldDir, TensorType& tensor) { // transform physical point to index coordinates itk::Index<3> idx; itk::ContinuousIndex< float, 3> cIdx; m_FaImage->TransformPhysicalPointToIndex(itkP, idx); m_FaImage->TransformPhysicalPointToContinuousIndex(itkP, cIdx); vnl_vector_fixed dir; dir.fill(0.0); if ( !m_FaImage->GetLargestPossibleRegion().IsInside(idx) ) return dir; int image_num = -1; if (!m_Parameters->m_InterpolateTractographyData) { dir = GetMatchingDirection(idx, oldDir, image_num); if (image_num>=0) tensor = m_TensorImages[image_num]->GetPixel(idx) * m_EmaxImage[image_num]->GetPixel(idx); } else { float frac_x = cIdx[0] - idx[0]; float frac_y = cIdx[1] - idx[1]; float frac_z = cIdx[2] - idx[2]; if (frac_x<0) { idx[0] -= 1; frac_x += 1; } if (frac_y<0) { idx[1] -= 1; frac_y += 1; } if (frac_z<0) { idx[2] -= 1; frac_z += 1; } frac_x = 1-frac_x; frac_y = 1-frac_y; frac_z = 1-frac_z; // int coordinates inside image? if (idx[0] >= 0 && idx[0] < static_cast(m_FaImage->GetLargestPossibleRegion().GetSize(0) - 1) && idx[1] >= 0 && idx[1] < static_cast(m_FaImage->GetLargestPossibleRegion().GetSize(1) - 1) && idx[2] >= 0 && idx[2] < static_cast(m_FaImage->GetLargestPossibleRegion().GetSize(2) - 1)) { // trilinear interpolation vnl_vector_fixed interpWeights; interpWeights[0] = ( frac_x)*( frac_y)*( frac_z); interpWeights[1] = (1-frac_x)*( frac_y)*( frac_z); interpWeights[2] = ( frac_x)*(1-frac_y)*( frac_z); interpWeights[3] = ( frac_x)*( frac_y)*(1-frac_z); interpWeights[4] = (1-frac_x)*(1-frac_y)*( frac_z); interpWeights[5] = ( frac_x)*(1-frac_y)*(1-frac_z); interpWeights[6] = (1-frac_x)*( frac_y)*(1-frac_z); interpWeights[7] = (1-frac_x)*(1-frac_y)*(1-frac_z); dir = GetMatchingDirection(idx, oldDir, image_num) * interpWeights[0]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(idx) * m_EmaxImage[image_num]->GetPixel(idx) * interpWeights[0]; itk::Index<3> tmpIdx = idx; tmpIdx[0]++; dir += GetMatchingDirection(tmpIdx, oldDir, image_num) * interpWeights[1]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(tmpIdx) * m_EmaxImage[image_num]->GetPixel(tmpIdx) * interpWeights[1]; tmpIdx = idx; tmpIdx[1]++; dir += GetMatchingDirection(tmpIdx, oldDir, image_num) * interpWeights[2]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(tmpIdx) * m_EmaxImage[image_num]->GetPixel(tmpIdx) * interpWeights[2]; tmpIdx = idx; tmpIdx[2]++; dir += GetMatchingDirection(tmpIdx, oldDir, image_num) * interpWeights[3]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(tmpIdx) * m_EmaxImage[image_num]->GetPixel(tmpIdx) * interpWeights[3]; tmpIdx = idx; tmpIdx[0]++; tmpIdx[1]++; dir += GetMatchingDirection(tmpIdx, oldDir, image_num) * interpWeights[4]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(tmpIdx) * m_EmaxImage[image_num]->GetPixel(tmpIdx) * interpWeights[4]; tmpIdx = idx; tmpIdx[1]++; tmpIdx[2]++; dir += GetMatchingDirection(tmpIdx, oldDir, image_num) * interpWeights[5]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(tmpIdx) * m_EmaxImage[image_num]->GetPixel(tmpIdx) * interpWeights[5]; tmpIdx = idx; tmpIdx[2]++; tmpIdx[0]++; dir += GetMatchingDirection(tmpIdx, oldDir, image_num) * interpWeights[6]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(tmpIdx) * m_EmaxImage[image_num]->GetPixel(tmpIdx) * interpWeights[6]; tmpIdx = idx; tmpIdx[0]++; tmpIdx[1]++; tmpIdx[2]++; dir += GetMatchingDirection(tmpIdx, oldDir, image_num) * interpWeights[7]; if (image_num>=0) tensor += m_TensorImages[image_num]->GetPixel(tmpIdx) * m_EmaxImage[image_num]->GetPixel(tmpIdx) * interpWeights[7]; } } return dir; } vnl_vector_fixed TrackingHandlerTensor::GetLargestEigenvector(TensorType& tensor) { vnl_vector_fixed dir; TensorType::EigenValuesArrayType eigenvalues; TensorType::EigenVectorsMatrixType eigenvectors; tensor.ComputeEigenAnalysis(eigenvalues, eigenvectors); dir[0] = eigenvectors(2, 0); dir[1] = eigenvectors(2, 1); dir[2] = eigenvectors(2, 2); if (dir.magnitude() TrackingHandlerTensor::ProposeDirection(const itk::Point& pos, std::deque >& olddirs, itk::Index<3>& oldIndex) { vnl_vector_fixed output_direction; output_direction.fill(0); TensorType tensor; tensor.Fill(0); try { itk::Index<3> index; m_TensorImages.at(0)->TransformPhysicalPointToIndex(pos, index); float fa = mitk::imv::GetImageValue(pos, m_Parameters->m_InterpolateTractographyData, m_FaInterpolator); if (fam_Cutoff) return output_direction; vnl_vector_fixed oldDir; oldDir.fill(0.0); if (!olddirs.empty()) oldDir = olddirs.back(); if (m_Parameters->m_FlipX) oldDir[0] *= -1; if (m_Parameters->m_FlipY) oldDir[1] *= -1; if (m_Parameters->m_FlipZ) oldDir[2] *= -1; float old_mag = oldDir.magnitude(); if (!m_Parameters->m_InterpolateTractographyData && oldIndex==index) return oldDir; output_direction = GetDirection(pos, oldDir, tensor); float mag = output_direction.magnitude(); if (mag>=mitk::eps) { output_direction.normalize(); if (old_mag>0.5 && m_Parameters->m_G>mitk::eps) // TEND tracking { output_direction[0] = m_Parameters->m_F*output_direction[0] + (1-m_Parameters->m_F)*( (1-m_Parameters->m_G)*oldDir[0] + m_Parameters->m_G*(tensor[0]*oldDir[0] + tensor[1]*oldDir[1] + tensor[2]*oldDir[2])); output_direction[1] = m_Parameters->m_F*output_direction[1] + (1-m_Parameters->m_F)*( (1-m_Parameters->m_G)*oldDir[1] + m_Parameters->m_G*(tensor[1]*oldDir[0] + tensor[3]*oldDir[1] + tensor[4]*oldDir[2])); output_direction[2] = m_Parameters->m_F*output_direction[2] + (1-m_Parameters->m_F)*( (1-m_Parameters->m_G)*oldDir[2] + m_Parameters->m_G*(tensor[2]*oldDir[0] + tensor[4]*oldDir[1] + tensor[5]*oldDir[2])); output_direction.normalize(); } float a = 1; if (old_mag>0.5) a = dot_product(output_direction, oldDir); if (a>=m_Parameters->GetAngularThreshold()) output_direction *= mag; else output_direction.fill(0); } else output_direction.fill(0); } catch(...) { } if (m_Parameters->m_FlipX) output_direction[0] *= -1; if (m_Parameters->m_FlipY) output_direction[1] *= -1; if (m_Parameters->m_FlipZ) output_direction[2] *= -1; if (m_Parameters->m_ApplyDirectionMatrix) output_direction = m_FloatImageRotation*output_direction; return output_direction; } } diff --git a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp index 92fe1c7996..990ee53b0e 100644 --- a/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp +++ b/Modules/DiffusionImaging/FiberTracking/Algorithms/itkStreamlineTrackingFilter.cpp @@ -1,986 +1,977 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include "itkStreamlineTrackingFilter.h" #include #include #include #include "itkPointShell.h" #include #include #include #include #include #include #include #include namespace itk { StreamlineTrackingFilter ::StreamlineTrackingFilter() : m_PauseTracking(false) , m_AbortTracking(false) , m_BuildFibersFinished(false) , m_BuildFibersReady(0) , m_FiberPolyData(nullptr) , m_Points(nullptr) , m_Cells(nullptr) , m_StoppingRegions(nullptr) , m_TargetRegions(nullptr) , m_SeedImage(nullptr) , m_MaskImage(nullptr) , m_ExclusionRegions(nullptr) , m_OutputProbabilityMap(nullptr) , m_MinVoxelSize(-1) , m_Verbose(true) , m_DemoMode(false) , m_CurrentTracts(0) , m_Progress(0) , m_StopTracking(false) , m_TrackingPriorHandler(nullptr) { this->SetNumberOfRequiredInputs(0); } std::string StreamlineTrackingFilter::GetStatusText() { std::string status = "Seedpoints processed: " + boost::lexical_cast(m_Progress) + "/" + boost::lexical_cast(m_SeedPoints.size()); if (m_SeedPoints.size()>0) status += " (" + boost::lexical_cast(100*m_Progress/m_SeedPoints.size()) + "%)"; if (m_Parameters->m_MaxNumFibers>0) status += "\nFibers accepted: " + boost::lexical_cast(m_CurrentTracts) + "/" + boost::lexical_cast(m_Parameters->m_MaxNumFibers); else status += "\nFibers accepted: " + boost::lexical_cast(m_CurrentTracts); return status; } void StreamlineTrackingFilter::BeforeTracking() { - itk::Vector< double, 3 > imageSpacing = m_TrackingHandler->GetSpacing(); - if(imageSpacing[0](imageSpacing[0]); - else if (imageSpacing[1] < imageSpacing[2]) - m_MinVoxelSize = static_cast(imageSpacing[1]); - else - m_MinVoxelSize = static_cast(imageSpacing[2]); - - m_Parameters->SetMinVoxelSize(m_MinVoxelSize); - m_StopTracking = false; m_TrackingHandler->SetParameters(m_Parameters); m_TrackingHandler->InitForTracking(); m_FiberPolyData = PolyDataType::New(); m_Points = vtkSmartPointer< vtkPoints >::New(); m_Cells = vtkSmartPointer< vtkCellArray >::New(); if (m_TrackingPriorHandler!=nullptr) { m_TrackingPriorHandler->InitForTracking(); } m_PolyDataContainer.clear(); for (unsigned int i=0; iGetNumberOfThreads(); i++) { PolyDataType poly = PolyDataType::New(); m_PolyDataContainer.push_back(poly); } + auto imageSpacing = m_TrackingHandler->GetSpacing(); if (m_Parameters->m_OutputProbMap) { m_OutputProbabilityMap = ItkDoubleImgType::New(); m_OutputProbabilityMap->SetSpacing(imageSpacing); m_OutputProbabilityMap->SetOrigin(m_TrackingHandler->GetOrigin()); m_OutputProbabilityMap->SetDirection(m_TrackingHandler->GetDirection()); m_OutputProbabilityMap->SetRegions(m_TrackingHandler->GetLargestPossibleRegion()); m_OutputProbabilityMap->Allocate(); m_OutputProbabilityMap->FillBuffer(0); } m_MaskInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_StopInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_SeedInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_TargetInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); m_ExclusionInterpolator = itk::LinearInterpolateImageFunction< ItkFloatImgType, float >::New(); if (m_StoppingRegions.IsNull()) { m_StoppingRegions = ItkFloatImgType::New(); m_StoppingRegions->SetSpacing( imageSpacing ); m_StoppingRegions->SetOrigin( m_TrackingHandler->GetOrigin() ); m_StoppingRegions->SetDirection( m_TrackingHandler->GetDirection() ); m_StoppingRegions->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_StoppingRegions->Allocate(); m_StoppingRegions->FillBuffer(0); } else std::cout << "StreamlineTracking - Using stopping region image" << std::endl; m_StopInterpolator->SetInputImage(m_StoppingRegions); if (m_ExclusionRegions.IsNotNull()) { std::cout << "StreamlineTracking - Using exclusion region image" << std::endl; m_ExclusionInterpolator->SetInputImage(m_ExclusionRegions); } if (m_TargetRegions.IsNull()) { m_TargetImageSet = false; m_TargetRegions = ItkFloatImgType::New(); m_TargetRegions->SetSpacing( imageSpacing ); m_TargetRegions->SetOrigin( m_TrackingHandler->GetOrigin() ); m_TargetRegions->SetDirection( m_TrackingHandler->GetDirection() ); m_TargetRegions->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_TargetRegions->Allocate(); m_TargetRegions->FillBuffer(1); } else { m_TargetImageSet = true; m_TargetInterpolator->SetInputImage(m_TargetRegions); std::cout << "StreamlineTracking - Using target region image" << std::endl; } if (m_SeedImage.IsNull()) { m_SeedImageSet = false; m_SeedImage = ItkFloatImgType::New(); m_SeedImage->SetSpacing( imageSpacing ); m_SeedImage->SetOrigin( m_TrackingHandler->GetOrigin() ); m_SeedImage->SetDirection( m_TrackingHandler->GetDirection() ); m_SeedImage->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_SeedImage->Allocate(); m_SeedImage->FillBuffer(1); } else { m_SeedImageSet = true; std::cout << "StreamlineTracking - Using seed image" << std::endl; } m_SeedInterpolator->SetInputImage(m_SeedImage); if (m_MaskImage.IsNull()) { // initialize mask image m_MaskImage = ItkFloatImgType::New(); m_MaskImage->SetSpacing( imageSpacing ); m_MaskImage->SetOrigin( m_TrackingHandler->GetOrigin() ); m_MaskImage->SetDirection( m_TrackingHandler->GetDirection() ); m_MaskImage->SetRegions( m_TrackingHandler->GetLargestPossibleRegion() ); m_MaskImage->Allocate(); m_MaskImage->FillBuffer(1); } else std::cout << "StreamlineTracking - Using mask image" << std::endl; m_MaskInterpolator->SetInputImage(m_MaskImage); // Autosettings for endpoint constraints if (m_Parameters->m_EpConstraints==EndpointConstraints::NONE && m_TargetImageSet && m_SeedImageSet) { MITK_INFO << "No endpoint constraint chosen but seed and target image set --> setting constraint to EPS_IN_SEED_AND_TARGET"; m_Parameters->m_EpConstraints = EndpointConstraints::EPS_IN_SEED_AND_TARGET; } else if (m_Parameters->m_EpConstraints==EndpointConstraints::NONE && m_TargetImageSet) { MITK_INFO << "No endpoint constraint chosen but target image set --> setting constraint to EPS_IN_TARGET"; m_Parameters->m_EpConstraints = EndpointConstraints::EPS_IN_TARGET; } // Check if endpoint constraints are valid FiberType test_fib; itk::Point p; p.Fill(0); test_fib.push_back(p); test_fib.push_back(p); IsValidFiber(&test_fib); if (m_SeedPoints.empty()) GetSeedPointsFromSeedImage(); m_BuildFibersReady = 0; m_BuildFibersFinished = false; m_Tractogram.clear(); m_SamplingPointset = mitk::PointSet::New(); m_AlternativePointset = mitk::PointSet::New(); m_StopVotePointset = mitk::PointSet::New(); m_StartTime = std::chrono::system_clock::now(); if (m_DemoMode) omp_set_num_threads(1); if (m_Parameters->m_Mode==mitk::TrackingDataHandler::MODE::DETERMINISTIC) std::cout << "StreamlineTracking - Mode: deterministic" << std::endl; else if(m_Parameters->m_Mode==mitk::TrackingDataHandler::MODE::PROBABILISTIC) { std::cout << "StreamlineTracking - Mode: probabilistic" << std::endl; std::cout << "StreamlineTracking - Trials per seed: " << m_Parameters->m_TrialsPerSeed << std::endl; } else std::cout << "StreamlineTracking - Mode: ???" << std::endl; if (m_Parameters->m_EpConstraints==EndpointConstraints::NONE) std::cout << "StreamlineTracking - Endpoint constraint: NONE" << std::endl; else if (m_Parameters->m_EpConstraints==EndpointConstraints::EPS_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_TARGET" << std::endl; else if (m_Parameters->m_EpConstraints==EndpointConstraints::EPS_IN_TARGET_LABELDIFF) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_TARGET_LABELDIFF" << std::endl; else if (m_Parameters->m_EpConstraints==EndpointConstraints::EPS_IN_SEED_AND_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: EPS_IN_SEED_AND_TARGET" << std::endl; else if (m_Parameters->m_EpConstraints==EndpointConstraints::MIN_ONE_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: MIN_ONE_EP_IN_TARGET" << std::endl; else if (m_Parameters->m_EpConstraints==EndpointConstraints::ONE_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: ONE_EP_IN_TARGET" << std::endl; else if (m_Parameters->m_EpConstraints==EndpointConstraints::NO_EP_IN_TARGET) std::cout << "StreamlineTracking - Endpoint constraint: NO_EP_IN_TARGET" << std::endl; std::cout << "StreamlineTracking - Angular threshold: " << m_Parameters->GetAngularThreshold() << "°" << std::endl; std::cout << "StreamlineTracking - Stepsize: " << m_Parameters->GetStepSize() << "mm (" << m_Parameters->GetStepSize()/m_MinVoxelSize << "*vox)" << std::endl; std::cout << "StreamlineTracking - Seeds per voxel: " << m_Parameters->m_SeedsPerVoxel << std::endl; std::cout << "StreamlineTracking - Max. tract length: " << m_Parameters->m_MaxTractLength << "mm" << std::endl; std::cout << "StreamlineTracking - Min. tract length: " << m_Parameters->m_MinTractLength << "mm" << std::endl; std::cout << "StreamlineTracking - Max. num. tracts: " << m_Parameters->m_MaxNumFibers << std::endl; std::cout << "StreamlineTracking - Loop check: " << m_Parameters->GetLoopCheckDeg() << "°" << std::endl; std::cout << "StreamlineTracking - Num. neighborhood samples: " << m_Parameters->m_NumSamples << std::endl; std::cout << "StreamlineTracking - Max. sampling distance: " << m_Parameters->GetSamplingDistance() << "mm (" << m_Parameters->GetSamplingDistance()/m_MinVoxelSize << "*vox)" << std::endl; std::cout << "StreamlineTracking - Deflection modifier: " << m_Parameters->m_DeflectionMod << std::endl; std::cout << "StreamlineTracking - Use stop votes: " << m_Parameters->m_StopVotes << std::endl; std::cout << "StreamlineTracking - Only frontal samples: " << m_Parameters->m_OnlyForwardSamples << std::endl; if (m_TrackingPriorHandler!=nullptr) std::cout << "StreamlineTracking - Using directional prior for tractography (w=" << m_Parameters->m_Weight << ")" << std::endl; if (m_DemoMode) { std::cout << "StreamlineTracking - Running in demo mode"; std::cout << "StreamlineTracking - Starting streamline tracking using 1 thread" << std::endl; } else std::cout << "StreamlineTracking - Starting streamline tracking using " << omp_get_max_threads() << " threads" << std::endl; } void StreamlineTrackingFilter::CalculateNewPosition(itk::Point& pos, vnl_vector_fixed& dir) { pos[0] += dir[0]*m_Parameters->GetStepSize(); pos[1] += dir[1]*m_Parameters->GetStepSize(); pos[2] += dir[2]*m_Parameters->GetStepSize(); } std::vector< vnl_vector_fixed > StreamlineTrackingFilter::CreateDirections(unsigned int NPoints) { std::vector< vnl_vector_fixed > pointshell; if (NPoints<2) return pointshell; std::vector< double > theta; theta.resize(NPoints); std::vector< double > phi; phi.resize(NPoints); auto C = sqrt(4*itk::Math::pi); phi[0] = 0.0; phi[NPoints-1] = 0.0; for(unsigned int i=0; i0 && i d; d[0] = static_cast(cos(theta[i]) * cos(phi[i])); d[1] = static_cast(cos(theta[i]) * sin(phi[i])); d[2] = static_cast(sin(theta[i])); pointshell.push_back(d); } return pointshell; } vnl_vector_fixed StreamlineTrackingFilter::GetNewDirection(const itk::Point &pos, std::deque >& olddirs, itk::Index<3> &oldIndex) { if (m_DemoMode) { m_SamplingPointset->Clear(); m_AlternativePointset->Clear(); m_StopVotePointset->Clear(); } vnl_vector_fixed direction; direction.fill(0); if (mitk::imv::IsInsideMask(pos, m_Parameters->m_InterpolateRoiImages, m_MaskInterpolator) && !mitk::imv::IsInsideMask(pos, m_Parameters->m_InterpolateRoiImages, m_StopInterpolator)) direction = m_TrackingHandler->ProposeDirection(pos, olddirs, oldIndex); // get direction proposal at current streamline position else return direction; int stop_votes = 0; int possible_stop_votes = 0; if (!olddirs.empty()) { vnl_vector_fixed olddir = olddirs.back(); std::vector< vnl_vector_fixed > probeVecs = CreateDirections(m_Parameters->m_NumSamples); itk::Point sample_pos; unsigned int alternatives = 1; for (unsigned int i=0; i d; bool is_stop_voter = false; if (!m_Parameters->m_FixRandomSeed && m_Parameters->m_RandomSampling) { d[0] = static_cast(m_TrackingHandler->GetRandDouble(-0.5, 0.5)); d[1] = static_cast(m_TrackingHandler->GetRandDouble(-0.5, 0.5)); d[2] = static_cast(m_TrackingHandler->GetRandDouble(-0.5, 0.5)); d.normalize(); d *= static_cast(m_TrackingHandler->GetRandDouble(0, static_cast(m_Parameters->GetSamplingDistance()))); } else { d = probeVecs.at(i); float dot = dot_product(d, olddir); if (m_Parameters->m_StopVotes && dot>0.7f) { is_stop_voter = true; possible_stop_votes++; } else if (m_Parameters->m_OnlyForwardSamples && dot<0) continue; d *= m_Parameters->GetSamplingDistance(); } sample_pos[0] = pos[0] + d[0]; sample_pos[1] = pos[1] + d[1]; sample_pos[2] = pos[2] + d[2]; vnl_vector_fixed tempDir; tempDir.fill(0.0); if (mitk::imv::IsInsideMask(sample_pos, m_Parameters->m_InterpolateRoiImages, m_MaskInterpolator)) tempDir = m_TrackingHandler->ProposeDirection(sample_pos, olddirs, oldIndex); // sample neighborhood if (tempDir.magnitude()>static_cast(mitk::eps)) { direction += tempDir; if(m_DemoMode) m_SamplingPointset->InsertPoint(i, sample_pos); } else if (m_Parameters->m_AvoidStop && olddir.magnitude()>0.5f) // out of white matter { if (is_stop_voter) stop_votes++; if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); float dot = dot_product(d, olddir); if (dot >= 0.0f) // in front of plane defined by pos and olddir d = -d + 2*dot*olddir; // reflect else d = -d; // invert // look a bit further into the other direction sample_pos[0] = pos[0] + d[0]; sample_pos[1] = pos[1] + d[1]; sample_pos[2] = pos[2] + d[2]; alternatives++; vnl_vector_fixed tempDir; tempDir.fill(0.0); if (mitk::imv::IsInsideMask(sample_pos, m_Parameters->m_InterpolateRoiImages, m_MaskInterpolator)) tempDir = m_TrackingHandler->ProposeDirection(sample_pos, olddirs, oldIndex); // sample neighborhood if (tempDir.magnitude()>static_cast(mitk::eps)) // are we back in the white matter? { direction += d * m_Parameters->m_DeflectionMod; // go into the direction of the white matter direction += tempDir; // go into the direction of the white matter direction at this location if(m_DemoMode) m_AlternativePointset->InsertPoint(alternatives, sample_pos); } else { if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); } } else { if (m_DemoMode) m_StopVotePointset->InsertPoint(i, sample_pos); if (is_stop_voter) stop_votes++; } } } bool valid = false; if (direction.magnitude()>0.001f && (possible_stop_votes==0 || static_cast(stop_votes)/possible_stop_votes<0.5f) ) { direction.normalize(); valid = true; } else direction.fill(0); if (m_TrackingPriorHandler!=nullptr && (m_Parameters->m_NewDirectionsFromPrior || valid)) { vnl_vector_fixed prior = m_TrackingPriorHandler->ProposeDirection(pos, olddirs, oldIndex); if (prior.magnitude()>0.001f) { prior.normalize(); if (dot_product(prior,direction)<0) prior *= -1; direction = (1.0f-m_Parameters->m_Weight) * direction + m_Parameters->m_Weight * prior; direction.normalize(); } else if (m_Parameters->m_RestrictToPrior) direction.fill(0.0); } return direction; } float StreamlineTrackingFilter::FollowStreamline(itk::Point pos, vnl_vector_fixed dir, FiberType* fib, DirectionContainer* container, float tractLength, bool front, bool &exclude) { vnl_vector_fixed zero_dir; zero_dir.fill(0.0); std::deque< vnl_vector_fixed > last_dirs; for (unsigned int i=0; im_NumPreviousDirections-1; i++) last_dirs.push_back(zero_dir); for (int step=0; step< 5000; step++) { itk::Index<3> oldIndex; m_TrackingHandler->WorldToIndex(pos, oldIndex); // get new position CalculateNewPosition(pos, dir); if (m_ExclusionRegions.IsNotNull() && mitk::imv::IsInsideMask(pos, m_Parameters->m_InterpolateRoiImages, m_ExclusionInterpolator)) { exclude = true; return tractLength; } if (m_AbortTracking) return tractLength; // if yes, add new point to streamline dir.normalize(); if (front) { fib->push_front(pos); container->push_front(dir); } else { fib->push_back(pos); container->push_back(dir); } tractLength += m_Parameters->GetStepSize(); if (m_Parameters->GetLoopCheckDeg()>=0 && CheckCurvature(container, front)>m_Parameters->GetLoopCheckDeg()) return tractLength; if (tractLength>m_Parameters->m_MaxTractLength) return tractLength; if (m_DemoMode && !m_Parameters->m_OutputProbMap) // CHECK: warum sind die samplingpunkte der streamline in der visualisierung immer einen schritt voras? { #pragma omp critical { m_BuildFibersReady++; m_Tractogram.push_back(*fib); BuildFibers(true); m_Stop = true; while (m_Stop){ } } } last_dirs.push_back(dir); if (last_dirs.size()>m_Parameters->m_NumPreviousDirections) last_dirs.pop_front(); dir = GetNewDirection(pos, last_dirs, oldIndex); while (m_PauseTracking){} if (dir.magnitude()<0.0001f) return tractLength; } return tractLength; } float StreamlineTrackingFilter::CheckCurvature(DirectionContainer* fib, bool front) { if (fib->size()<8) return 0; float m_Distance = std::max(m_MinVoxelSize*4, m_Parameters->GetStepSize()*8); float dist = 0; std::vector< vnl_vector_fixed< float, 3 > > vectors; vnl_vector_fixed< float, 3 > meanV; meanV.fill(0); float dev = 0; if (front) { int c = 0; while(dist(fib->size())-1) { dist += m_Parameters->GetStepSize(); vnl_vector_fixed< float, 3 > v = fib->at(static_cast(c)); if (dot_product(v,meanV)<0) v = -v; vectors.push_back(v); meanV += v; c++; } } else { int c = static_cast(fib->size())-1; while(dist=0) { dist += m_Parameters->GetStepSize(); vnl_vector_fixed< float, 3 > v = fib->at(static_cast(c)); if (dot_product(v,meanV)<0) v = -v; vectors.push_back(v); meanV += v; c--; } } meanV.normalize(); for (unsigned int c=0; c1.0f) angle = 1.0; dev += acos(angle)*180.0f/static_cast(itk::Math::pi); } if (vectors.size()>0) dev /= vectors.size(); return dev; } std::shared_ptr StreamlineTrackingFilter::GetParameters() const { return m_Parameters; } void StreamlineTrackingFilter::SetParameters(std::shared_ptr< mitk::StreamlineTractographyParameters > Parameters) { m_Parameters = Parameters; } void StreamlineTrackingFilter::SetTrackingPriorHandler(mitk::TrackingDataHandler *TrackingPriorHandler) { m_TrackingPriorHandler = TrackingPriorHandler; } void StreamlineTrackingFilter::GetSeedPointsFromSeedImage() { MITK_INFO << "StreamlineTracking - Calculating seed points."; m_SeedPoints.clear(); typedef ImageRegionConstIterator< ItkFloatImgType > MaskIteratorType; MaskIteratorType sit(m_SeedImage, m_SeedImage->GetLargestPossibleRegion()); sit.GoToBegin(); while (!sit.IsAtEnd()) { if (sit.Value()>0) { ItkFloatImgType::IndexType index = sit.GetIndex(); itk::ContinuousIndex start; start[0] = index[0]; start[1] = index[1]; start[2] = index[2]; itk::Point worldPos; m_SeedImage->TransformContinuousIndexToPhysicalPoint(start, worldPos); if ( mitk::imv::IsInsideMask(worldPos, m_Parameters->m_InterpolateRoiImages, m_MaskInterpolator) ) { m_SeedPoints.push_back(worldPos); for (unsigned int s = 1; s < m_Parameters->m_SeedsPerVoxel; s++) { start[0] = index[0] + static_cast(m_TrackingHandler->GetRandDouble(-0.5, 0.5)); start[1] = index[1] + static_cast(m_TrackingHandler->GetRandDouble(-0.5, 0.5)); start[2] = index[2] + static_cast(m_TrackingHandler->GetRandDouble(-0.5, 0.5)); itk::Point worldPos; m_SeedImage->TransformContinuousIndexToPhysicalPoint(start, worldPos); m_SeedPoints.push_back(worldPos); } } } ++sit; } if (m_SeedPoints.empty()) mitkThrow() << "No valid seed point in seed image! Is your seed image registered with the image you are tracking on?"; } void StreamlineTrackingFilter::GenerateData() { this->BeforeTracking(); if (!m_Parameters->m_FixRandomSeed) std::random_shuffle(m_SeedPoints.begin(), m_SeedPoints.end()); m_CurrentTracts = 0; int num_seeds = static_cast(m_SeedPoints.size()); itk::Index<3> zeroIndex; zeroIndex.Fill(0); m_Progress = 0; int i = 0; int print_interval = num_seeds/100; if (print_interval<100) m_Verbose=false; #pragma omp parallel while (i=num_seeds || m_StopTracking) continue; else if (m_Verbose && i%print_interval==0) #pragma omp critical { m_Progress += static_cast(print_interval); std::cout << " \r"; if (m_Parameters->m_MaxNumFibers>0) std::cout << "Tried: " << m_Progress << "/" << num_seeds << " | Accepted: " << m_CurrentTracts << "/" << m_Parameters->m_MaxNumFibers << '\r'; else std::cout << "Tried: " << m_Progress << "/" << num_seeds << " | Accepted: " << m_CurrentTracts << '\r'; cout.flush(); } const itk::Point worldPos = m_SeedPoints.at(static_cast(temp_i)); for (unsigned int trials=0; trialsm_TrialsPerSeed; ++trials) { FiberType fib; DirectionContainer direction_container; float tractLength = 0; unsigned long counter = 0; // get starting direction vnl_vector_fixed dir; dir.fill(0.0); std::deque< vnl_vector_fixed > olddirs; dir = GetNewDirection(worldPos, olddirs, zeroIndex) * 0.5f; bool exclude = false; if (m_ExclusionRegions.IsNotNull() && mitk::imv::IsInsideMask(worldPos, m_Parameters->m_InterpolateRoiImages, m_ExclusionInterpolator)) exclude = true; bool success = false; if (dir.magnitude()>0.0001f && !exclude) { // forward tracking tractLength = FollowStreamline(worldPos, dir, &fib, &direction_container, 0, false, exclude); fib.push_front(worldPos); // backward tracking if (!exclude) tractLength = FollowStreamline(worldPos, -dir, &fib, &direction_container, tractLength, true, exclude); counter = fib.size(); if (tractLength>=m_Parameters->m_MinTractLength && counter>=2 && !exclude) { #pragma omp critical if ( IsValidFiber(&fib) ) { if (!m_StopTracking) { if (!m_Parameters->m_OutputProbMap) m_Tractogram.push_back(fib); else FiberToProbmap(&fib); m_CurrentTracts++; success = true; } if (m_Parameters->m_MaxNumFibers > 0 && m_CurrentTracts>=static_cast(m_Parameters->m_MaxNumFibers)) { if (!m_StopTracking) { std::cout << " \r"; MITK_INFO << "Reconstructed maximum number of tracts (" << m_CurrentTracts << "). Stopping tractography."; } m_StopTracking = true; } } } } if (success || m_Parameters->m_Mode!=MODE::PROBABILISTIC) break; // we only try one seed point multiple times if we use a probabilistic tracker and have not found a valid streamline yet }// trials per seed }// seed points this->AfterTracking(); } bool StreamlineTrackingFilter::IsValidFiber(FiberType* fib) { if (m_Parameters->m_EpConstraints==EndpointConstraints::NONE) { return true; } else if (m_Parameters->m_EpConstraints==EndpointConstraints::EPS_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) && mitk::imv::IsInsideMask(fib->back(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint EPS_IN_TARGET chosen!"; } else if (m_Parameters->m_EpConstraints==EndpointConstraints::EPS_IN_TARGET_LABELDIFF) { if (m_TargetImageSet) { float v1 = mitk::imv::GetImageValue(fib->front(), false, m_TargetInterpolator); float v2 = mitk::imv::GetImageValue(fib->back(), false, m_TargetInterpolator); if ( v1>0.0f && v2>0.0f && v1!=v2 ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint EPS_IN_TARGET_LABELDIFF chosen!"; } else if (m_Parameters->m_EpConstraints==EndpointConstraints::EPS_IN_SEED_AND_TARGET) { if (m_TargetImageSet && m_SeedImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_Parameters->m_InterpolateRoiImages, m_SeedInterpolator) && mitk::imv::IsInsideMask(fib->back(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) ) return true; if ( mitk::imv::IsInsideMask(fib->back(), m_Parameters->m_InterpolateRoiImages, m_SeedInterpolator) && mitk::imv::IsInsideMask(fib->front(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target or seed image set but endpoint constraint EPS_IN_SEED_AND_TARGET chosen!"; } else if (m_Parameters->m_EpConstraints==EndpointConstraints::MIN_ONE_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) || mitk::imv::IsInsideMask(fib->back(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint MIN_ONE_EP_IN_TARGET chosen!"; } else if (m_Parameters->m_EpConstraints==EndpointConstraints::ONE_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) && !mitk::imv::IsInsideMask(fib->back(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) ) return true; if ( !mitk::imv::IsInsideMask(fib->back(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) && mitk::imv::IsInsideMask(fib->front(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) ) return true; return false; } else mitkThrow() << "No target image set but endpoint constraint ONE_EP_IN_TARGET chosen!"; } else if (m_Parameters->m_EpConstraints==EndpointConstraints::NO_EP_IN_TARGET) { if (m_TargetImageSet) { if ( mitk::imv::IsInsideMask(fib->front(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) || mitk::imv::IsInsideMask(fib->back(), m_Parameters->m_InterpolateRoiImages, m_TargetInterpolator) ) return false; return true; } else mitkThrow() << "No target image set but endpoint constraint NO_EP_IN_TARGET chosen!"; } return true; } void StreamlineTrackingFilter::FiberToProbmap(FiberType* fib) { ItkDoubleImgType::IndexType last_idx; last_idx.Fill(0); for (auto p : *fib) { ItkDoubleImgType::IndexType idx; m_OutputProbabilityMap->TransformPhysicalPointToIndex(p, idx); if (idx != last_idx) { if (m_OutputProbabilityMap->GetLargestPossibleRegion().IsInside(idx)) m_OutputProbabilityMap->SetPixel(idx, m_OutputProbabilityMap->GetPixel(idx)+1); last_idx = idx; } } } void StreamlineTrackingFilter::BuildFibers(bool check) { if (m_BuildFibersReady::New(); vtkSmartPointer vNewLines = vtkSmartPointer::New(); vtkSmartPointer vNewPoints = vtkSmartPointer::New(); for (unsigned int i=0; i container = vtkSmartPointer::New(); FiberType fib = m_Tractogram.at(i); for (FiberType::iterator it = fib.begin(); it!=fib.end(); ++it) { vtkIdType id = vNewPoints->InsertNextPoint((*it).GetDataPointer()); container->GetPointIds()->InsertNextId(id); } vNewLines->InsertNextCell(container); } if (check) for (int i=0; iSetPoints(vNewPoints); m_FiberPolyData->SetLines(vNewLines); m_BuildFibersFinished = true; } void StreamlineTrackingFilter::AfterTracking() { if (m_Verbose) std::cout << " \r"; if (!m_Parameters->m_OutputProbMap) { MITK_INFO << "Reconstructed " << m_Tractogram.size() << " fibers."; MITK_INFO << "Generating polydata "; BuildFibers(false); } else { itk::RescaleIntensityImageFilter< ItkDoubleImgType, ItkDoubleImgType >::Pointer filter = itk::RescaleIntensityImageFilter< ItkDoubleImgType, ItkDoubleImgType >::New(); filter->SetInput(m_OutputProbabilityMap); filter->SetOutputMaximum(1.0); filter->SetOutputMinimum(0.0); filter->Update(); m_OutputProbabilityMap = filter->GetOutput(); } MITK_INFO << "done"; m_EndTime = std::chrono::system_clock::now(); std::chrono::hours hh = std::chrono::duration_cast(m_EndTime - m_StartTime); std::chrono::minutes mm = std::chrono::duration_cast(m_EndTime - m_StartTime); std::chrono::seconds ss = std::chrono::duration_cast(m_EndTime - m_StartTime); mm %= 60; ss %= 60; MITK_INFO << "Tracking took " << hh.count() << "h, " << mm.count() << "m and " << ss.count() << "s"; m_SeedPoints.clear(); } void StreamlineTrackingFilter::SetDicomProperties(mitk::FiberBundle::Pointer fib) { std::string model_code_value = "-"; std::string model_code_meaning = "-"; std::string algo_code_value = "-"; std::string algo_code_meaning = "-"; if ( m_Parameters->m_Mode==MODE::DETERMINISTIC && dynamic_cast(m_TrackingHandler) && !m_Parameters->m_InterpolateTractographyData) { algo_code_value = "sup181_ee04"; algo_code_meaning = "FACT"; } else if (m_Parameters->m_Mode==MODE::DETERMINISTIC) { algo_code_value = "sup181_ee01"; algo_code_meaning = "Deterministic"; } else if (m_Parameters->m_Mode==MODE::PROBABILISTIC) { algo_code_value = "sup181_ee02"; algo_code_meaning = "Probabilistic"; } if (dynamic_cast(m_TrackingHandler) || (dynamic_cast(m_TrackingHandler) && dynamic_cast(m_TrackingHandler)->GetIsOdfFromTensor() ) ) { if ( dynamic_cast(m_TrackingHandler) && dynamic_cast(m_TrackingHandler)->GetNumTensorImages()>1 ) { model_code_value = "sup181_bb02"; model_code_meaning = "Multi Tensor"; } else { model_code_value = "sup181_bb01"; model_code_meaning = "Single Tensor"; } } else if (dynamic_cast*>(m_TrackingHandler) || dynamic_cast*>(m_TrackingHandler)) { model_code_value = "sup181_bb03"; model_code_meaning = "Model Free"; } else if (dynamic_cast(m_TrackingHandler)) { model_code_value = "-"; model_code_meaning = "ODF"; } else if (dynamic_cast(m_TrackingHandler)) { model_code_value = "-"; model_code_meaning = "Peaks"; } fib->SetProperty("DICOM.anatomy.value", mitk::StringProperty::New("T-A0095")); fib->SetProperty("DICOM.anatomy.meaning", mitk::StringProperty::New("White matter of brain and spinal cord")); fib->SetProperty("DICOM.algo_code.value", mitk::StringProperty::New(algo_code_value)); fib->SetProperty("DICOM.algo_code.meaning", mitk::StringProperty::New(algo_code_meaning)); fib->SetProperty("DICOM.model_code.value", mitk::StringProperty::New(model_code_value)); fib->SetProperty("DICOM.model_code.meaning", mitk::StringProperty::New(model_code_meaning)); } } diff --git a/Modules/DiffusionImaging/FiberTracking/IODataStructures/mitkStreamlineTractographyParameters.h b/Modules/DiffusionImaging/FiberTracking/IODataStructures/mitkStreamlineTractographyParameters.h index 8ff9fdd9b5..7912218e1d 100644 --- a/Modules/DiffusionImaging/FiberTracking/IODataStructures/mitkStreamlineTractographyParameters.h +++ b/Modules/DiffusionImaging/FiberTracking/IODataStructures/mitkStreamlineTractographyParameters.h @@ -1,163 +1,166 @@ #pragma once /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include namespace mitk { /** * \brief Datastructure to manage streamline tractography parameters. * */ class MITKFIBERTRACKING_EXPORT StreamlineTractographyParameters { public: enum EndpointConstraints { NONE, ///< No constraints on endpoint locations EPS_IN_TARGET, ///< Both EPs are required to be located in the target image EPS_IN_TARGET_LABELDIFF, ///< Both EPs are required to be located in the target image and the image values at the respective position needs to be distinct EPS_IN_SEED_AND_TARGET, ///< One EP is required to be located in the seed image and one in the target image MIN_ONE_EP_IN_TARGET, ///< At least one EP is required to be located in the target image ONE_EP_IN_TARGET, ///< Exactly one EP is required to be located in the target image NO_EP_IN_TARGET ///< No EP is allowed to be located in the target image }; enum MODE { DETERMINISTIC, PROBABILISTIC }; typedef itk::Image ItkFloatImgType; typedef itk::Image ItkDoubleImgType; typedef itk::Image ItkUcharImgType; StreamlineTractographyParameters(); StreamlineTractographyParameters(const StreamlineTractographyParameters ¶ms) = default; ~StreamlineTractographyParameters(); void SaveParameters(std::string filename); ///< Save image generation parameters to .stp file. void LoadParameters(std::string filename); ///< Load image generation parameters from .stp file. template< class ParameterType > ParameterType ReadVal(boost::property_tree::ptree::value_type const& v, std::string tag, ParameterType defaultValue, bool essential=false); // seeding unsigned int m_SeedsPerVoxel = 1; unsigned int m_TrialsPerSeed = 10; int m_MaxNumFibers = -1; // - seed image // interactive float m_InteractiveRadius = 2; unsigned int m_NumInteractiveSeeds = 50; bool m_EnableInteractive = false; // ROI constraints EndpointConstraints m_EpConstraints; // - mask image // - stop image // - exclusion image // - target image // tractography MODE m_Mode; bool m_SharpenOdfs = false; float m_Cutoff = 0.1; // - fa/gfa image float m_OdfCutoff = 0.00025; float m_MinTractLength = 20; float m_MaxTractLength = 400; float m_F = 1; float m_G = 0; bool m_FixRandomSeed = false; unsigned int m_NumPreviousDirections = 1; // prior // - peak image float m_Weight = 0.5; bool m_RestrictToPrior = true; bool m_NewDirectionsFromPrior = true; + bool m_PriorFlipX = false; + bool m_PriorFlipY = false; + bool m_PriorFlipZ = false; // neighborhood sampling unsigned int m_NumSamples = 0; bool m_OnlyForwardSamples = false; bool m_StopVotes = false; bool m_AvoidStop = true; bool m_RandomSampling = false; float m_DeflectionMod = 1.0; // data handling bool m_FlipX = false; bool m_FlipY = false; bool m_FlipZ = false; bool m_InterpolateTractographyData = true; bool m_InterpolateRoiImages; bool m_ApplyDirectionMatrix = false; // output and postprocessing bool m_CompressFibers = true; float m_Compression = 0.1; bool m_OutputProbMap = false; float GetAngularThresholdDeg() const; void SetAngularThresholdDeg(float AngularThresholdDeg); float GetLoopCheckDeg() const; void SetLoopCheckDeg(float LoopCheckDeg); float GetStepSizeVox() const; void SetStepSizeVox(float StepSizeVox); void SetMinVoxelSize(float MinVoxelSize); float GetAngularThreshold() const; float GetSamplingDistance() const; void SetSamplingDistance(float SamplingDistance); float GetStepSize() const; private: void AutoAdjust(); float m_SamplingDistance = -1; float m_AngularThresholdDeg; float m_LoopCheckDeg; float m_StepSizeVox; float m_AngularThreshold; float m_LoopCheck; float m_StepSize; // mm float m_MinVoxelSize = 1.0; // float m_AngularThreshold = 0; // in deg // float m_LoopCheck = 0; // in deg // float m_StepSize = 0; }; } diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox index 8c9322383e..73168209fa 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/documentation/UserManual/QmitkStreamlineTrackingViewUserManual.dox @@ -1,102 +1,103 @@ /** \page org_mitk_views_streamlinetracking Streamline Tractography This view enables streamline tractography on various input data. The corresponding command line application is named "MitkStreamlineTractography". Available sections: - \ref StrTrackUserManualInputData - \ref StrTrackUserManualSeeding - \ref StrTrackUserManualConstraints - \ref StrTrackUserManualParameters - \ref StrTrackUserManualNeighbourhoodSampling - \ref StrTrackUserManualDataHandling - \ref StrTrackUserManualPostprocessing - \ref StrTrackUserManualReferences \section StrTrackUserManualInputData Input Data Select the data you want to track on in the datamanager. Supported file types are: - One or multiple DTI images selected in the datamanager. - One ODF image, e.g. obtained using MITK Q-ball reconstruction or MRtrix CSD (tractography similar to [6]). - One peak image (4D float image). - One raw diffusion-weighted image for machine learning based tractography [1]. -- Tractography Forest: Needed for machine learning based tractography [1]. \section StrTrackUserManualSeeding Seeding Specify how, where and how many tractography seed points are placed. This can be either done statically using a seed image or in an interactive fashion. Interactive tractography enables the dynamic placement of spherical seed regions simply by clicking into the image (similar to [5]). Image based seeding: - Seed Image: ROI image used to define the seed voxels. If no seed mask is specified, the whole image volume is seeded. - Seeds per voxel: If set to 1, the seed is defined as the voxel center. If > 1 the seeds are distributet randomly inside the voxel. Interactive seeding: - Update on Parameter Change: When "Update on Parameter Change" is checked, each parameter change causes an instant retracking with the new parameters. This enables an intuitive exploration of the effects that the other tractography parameters have on the resulting tractogram. - Radius: Radius of the manually placed spherical seed region. - Num.Seeds: Number of seeds placed randomly inside the spherical seed region. Parameters for both seeding modes: - Trials Per Seed: Try each seed N times until a valid streamline is obtained (only for probabilistic tractography). - Max. Num. Fibers: Tractography is stopped after the desired number of fibers is reached, even before all seed points are processed. \section StrTrackUserManualConstraints ROI Constraints Specify various ROI and mask images to constrain the tractography process. - Mask Image: ROI image used to constrain the generated streamlines, typically a brain mask. Streamlines that leave the regions defined in this image will stop immediately. - Stop ROI Image: ROI image used to define stopping regions. Streamlines that enter the regions defined in this image will stop immediately. - Exclusion ROI Image: Fibers that enter a region defined in this image will be discarded. - Endpoint Constraints: Determines which fibers are accepted based on their endpoint location. Options are: - No constraints on endpoint locations (command line option NONE) - Both EPs are required to be located in the target image (command line option EPS_IN_TARGET) - Both EPs are required to be located in the target image and the image values at the respective position needs to be distinct (command line option EPS_IN_TARGET_LABELDIFF) - One EP is required to be located in the seed image and one in the target image (command line option EPS_IN_SEED_AND_TARGET) - At least one EP is required to be located in the target image (command line option MIN_ONE_EP_IN_TARGET) - Exactly one EP is required to be located in the target image (command line option ONE_EP_IN_TARGET) - No EP is allowed to be located in the target image (command line option NO_EP_IN_TARGET) - Target Image: ROI image needed for endpoint constraints. \section StrTrackUserManualParameters Tractography Parameters -- Mode: Toggle between deterministic and probabilistic tractography. Peak tracking only supports deterministic mode. The probabilistic method simply samples the output direction from the discrete probability ditribution provided by the discretized ODF. +- Mode: Toggle between deterministic and probabilistic tractography (also affects tracking prior proposals). The probabilistic method simply samples the output direction from the discrete probability ditribution provided by the discretized ODF. Probabilistic peak tracking does not derive probabilities from the data but simply adds a normally distributed jitter to the proposed direction. - Sharpen ODFs: If you are using dODF images as input, it is advisable to sharpen the ODFs (min-max normalize and raise to the power of 4). This is not necessary (and not recommended) for CSD fODFs, since they are naturally much sharper. - Cutoff: If the streamline reaches a position with an FA value or peak magnitude lower than the speciefied threshold, tracking is terminated. Typical values are 0.2 for FA/GFA and 0.1 for CSD peaks. - FA/GFA image used to determine streamline termination. If no image is specified, the FA/GFA image is automatically calculated from the input image. If multiple tensor images are used as input, it is recommended to provide such an image since the FA maps calculated from the individual input tensor images can not provide a suitable termination criterion. - ODF Cutoff: Additional threshold on the ODF magnitude. This is useful in case of CSD fODF tractography. For fODFs a good default value is 0.1, for normalized dODFs, e.g. Q-ball ODFs, this threshold should be very low (0.00025) or 0. - Step Size: The algorithm proceeds along the streamline with a fixed stepsize. Default is 0.5*minSpacing. - Min. Tract Length: Shorter fibers are discarded. - Angular threshold: Maximum angle between two successive steps (in degree). Default is 90° * step_size. For probabilistic tractography, candidate directions exceeding this threshold have probability 0, i.e. the respective ODF value is set to zero. The probabilities of the valid directions are normalized to sum to 1. - Loop Check: Stop streamline if the threshold on the angular stdev over the last 4 voxel lengths is exceeded. -1 = no loop check. - f and g values to balance between FACT [2] and TEND [3,4] tracking (only for tensor based tractography). For further information please refer to [2,3] \section StrTrackUserManualTractographyPrior Tractography Prior It is possible to use a peak image as prior for tractography on arbitrary other input images. The local progression direction is determined as the weighted average between the direction obtained from the prior and the input data. - Weight: Weighting factor between prior and input data directions. A weight of zero means that no prior iformation is used. With a weight of one, tractography is performed directly on the prior directions itself. - Restrict to Prior: The prior image is used as tractography mask. Voxels without prior peaks are excluded. - New Directions from Prior: By default, the prior is used even if there is no valid direction found in the data. If unchecked, the prior cannot create directions where there are none in the data. +- Flip directions: Internally flips prior directions. This might be necessary depending on the input data. \section StrTrackUserManualDataHandling Data Handling - Flip directions: Internally flips progression directions. This might be necessary depending on the input data. - Interpolate Tractography Data: Trilinearly interpolate the input image used for tractography. - Interpolate ROI Images: Trilinearly interpolate the ROI images used to constrain the tractography. \section StrTrackUserManualNeighbourhoodSampling Neighbourhood Sampling (for details see [1]) - Neighborhood Samples: Number of neighborhood samples that are used to determine the next fiber progression direction. - Sampling Distance: Distance of the sampling positions from the current streamline position (in voxels). - Use Only Frontal Samples: Only neighborhood samples in front of the current streamline position are considered. - Use Stop-Votes: If checked, the majority of sampling points has to place a stop-vote for the streamline to terminate. If not checked, all sampling positions have to vote for a streamline termination. \section StrTrackUserManualPostprocessing Output and Postprocessing - Compress Fibers: Whole brain tractograms obtained with a small step size can contain billions of points. The tractograms can be compressed by removing points that do not really contribute to the fiber shape, such as many points on a straight line. An error threshold (in mm) can be defined to specify which points should be removed and which not. - Output Probability Map: No streamline are generated. Instead, the tractography outputs a visitation-count map that indicates the probability of a fiber to reach a voxel from the selected seed region. For this measure to be sensible, the number of seeds per voxel needs to be rather large. \section StrTrackUserManualReferences References [1] Neher, Peter F., Marc-Alexandre Côté, Jean-Christophe Houde, Maxime Descoteaux, and Klaus H. Maier-Hein. “Fiber Tractography Using Machine Learning.” NeuroImage. Accessed July 19, 2017. doi:10.1016/j.neuroimage.2017.07.028.\n [2] Mori, Susumu, Walter E. Kaufmann, Godfrey D. Pearlson, Barbara J. Crain, Bram Stieltjes, Meiyappan Solaiyappan, and Peter C. M. Van Zijl. “In Vivo Visualization of Human Neural Pathways by Magnetic Resonance Imaging.” Annals of Neurology 47 (2000): 412–414.\n [3] Weinstein, David, Gordon Kindlmann, and Eric Lundberg. “Tensorlines: Advection-Diffusion Based Propagation through Diffusion Tensor Fields.” In Proceedings of the Conference on Visualization’99: Celebrating Ten Years, 249–253, n.d.\n [4] Lazar, Mariana, David M. Weinstein, Jay S. Tsuruda, Khader M. Hasan, Konstantinos Arfanakis, M. Elizabeth Meyerand, Benham Badie, et al. “White Matter Tractography Using Diffusion Tensor Deflection.” Human Brain Mapping 18, no. 4 (2003): 306–321.\n [5] Chamberland, M., K. Whittingstall, D. Fortin, D. Mathieu, and M. Descoteaux. “Real-Time Multi-Peak Tractography for Instantaneous Connectivity Display.” Front Neuroinform 8 (2014): 59. doi:10.3389/fninf.2014.00059.\n [6] Tournier, J-Donald, Fernando Calamante, and Alan Connelly. “MRtrix: Diffusion Tractography in Crossing Fiber Regions.” International Journal of Imaging Systems and Technology 22, no. 1 (March 2012): 53–66. doi:10.1002/ima.22005. */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp index e00e0b7373..daee3d51d2 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp +++ b/Plugins/org.mitk.gui.qt.diffusionimaging.tractography/src/internal/QmitkStreamlineTrackingView.cpp @@ -1,1060 +1,1033 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ // Blueberry #include #include #include // Qmitk #include "QmitkStreamlineTrackingView.h" #include "QmitkStdMultiWidget.h" // Qt #include #include // MITK #include #include #include #include #include #include #include #include #include #include #include #include #include #include // VTK #include #include #include #include #include #include #include #include #include #include const std::string QmitkStreamlineTrackingView::VIEW_ID = "org.mitk.views.streamlinetracking"; const std::string id_DataManager = "org.mitk.views.datamanager"; using namespace berry; QmitkStreamlineTrackingWorker::QmitkStreamlineTrackingWorker(QmitkStreamlineTrackingView* view) : m_View(view) { } void QmitkStreamlineTrackingWorker::run() { m_View->m_Tracker->Update(); m_View->m_TrackingThread.quit(); } QmitkStreamlineTrackingView::QmitkStreamlineTrackingView() : m_TrackingWorker(this) , m_Controls(nullptr) , m_FirstTensorProbRun(true) , m_FirstInteractiveRun(true) , m_TrackingHandler(nullptr) , m_ThreadIsRunning(false) , m_DeleteTrackingHandler(false) , m_Visible(false) , m_LastPrior(nullptr) , m_TrackingPriorHandler(nullptr) { m_TrackingWorker.moveToThread(&m_TrackingThread); connect(&m_TrackingThread, SIGNAL(started()), this, SLOT(BeforeThread())); connect(&m_TrackingThread, SIGNAL(started()), &m_TrackingWorker, SLOT(run())); connect(&m_TrackingThread, SIGNAL(finished()), this, SLOT(AfterThread())); m_TrackingTimer = new QTimer(this); } // Destructor QmitkStreamlineTrackingView::~QmitkStreamlineTrackingView() { if (m_Tracker.IsNull()) return; m_Tracker->SetStopTracking(true); m_TrackingThread.wait(); } void QmitkStreamlineTrackingView::CreateQtPartControl( QWidget *parent ) { if ( !m_Controls ) { // create GUI widgets from the Qt Designer's .ui file m_Controls = new Ui::QmitkStreamlineTrackingViewControls; m_Controls->setupUi( parent ); m_Controls->m_FaImageSelectionWidget->SetDataStorage(this->GetDataStorage()); m_Controls->m_SeedImageSelectionWidget->SetDataStorage(this->GetDataStorage()); m_Controls->m_MaskImageSelectionWidget->SetDataStorage(this->GetDataStorage()); m_Controls->m_TargetImageSelectionWidget->SetDataStorage(this->GetDataStorage()); m_Controls->m_PriorImageSelectionWidget->SetDataStorage(this->GetDataStorage()); m_Controls->m_StopImageSelectionWidget->SetDataStorage(this->GetDataStorage()); m_Controls->m_ForestSelectionWidget->SetDataStorage(this->GetDataStorage()); m_Controls->m_ExclusionImageSelectionWidget->SetDataStorage(this->GetDataStorage()); mitk::TNodePredicateDataType::Pointer isPeakImagePredicate = mitk::TNodePredicateDataType::New(); mitk::TNodePredicateDataType::Pointer isImagePredicate = mitk::TNodePredicateDataType::New(); mitk::TNodePredicateDataType::Pointer isTractographyForest = mitk::TNodePredicateDataType::New(); mitk::NodePredicateProperty::Pointer isBinaryPredicate = mitk::NodePredicateProperty::New("binary", mitk::BoolProperty::New(true)); mitk::NodePredicateNot::Pointer isNotBinaryPredicate = mitk::NodePredicateNot::New( isBinaryPredicate ); mitk::NodePredicateAnd::Pointer isNotABinaryImagePredicate = mitk::NodePredicateAnd::New( isImagePredicate, isNotBinaryPredicate ); mitk::NodePredicateDimension::Pointer dimensionPredicate = mitk::NodePredicateDimension::New(3); m_Controls->m_ForestSelectionWidget->SetNodePredicate(isTractographyForest); m_Controls->m_FaImageSelectionWidget->SetNodePredicate( mitk::NodePredicateAnd::New(isNotABinaryImagePredicate, dimensionPredicate) ); m_Controls->m_FaImageSelectionWidget->SetEmptyInfo("--"); m_Controls->m_FaImageSelectionWidget->SetSelectionIsOptional(true); m_Controls->m_SeedImageSelectionWidget->SetNodePredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_SeedImageSelectionWidget->SetEmptyInfo("--"); m_Controls->m_SeedImageSelectionWidget->SetSelectionIsOptional(true); m_Controls->m_MaskImageSelectionWidget->SetNodePredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_MaskImageSelectionWidget->SetEmptyInfo("--"); m_Controls->m_MaskImageSelectionWidget->SetSelectionIsOptional(true); m_Controls->m_StopImageSelectionWidget->SetNodePredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_StopImageSelectionWidget->SetEmptyInfo("--"); m_Controls->m_StopImageSelectionWidget->SetSelectionIsOptional(true); m_Controls->m_TargetImageSelectionWidget->SetNodePredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_TargetImageSelectionWidget->SetEmptyInfo("--"); m_Controls->m_TargetImageSelectionWidget->SetSelectionIsOptional(true); m_Controls->m_PriorImageSelectionWidget->SetNodePredicate( isPeakImagePredicate ); m_Controls->m_PriorImageSelectionWidget->SetEmptyInfo("--"); m_Controls->m_PriorImageSelectionWidget->SetSelectionIsOptional(true); m_Controls->m_ExclusionImageSelectionWidget->SetNodePredicate( mitk::NodePredicateAnd::New(isImagePredicate, dimensionPredicate) ); m_Controls->m_ExclusionImageSelectionWidget->SetEmptyInfo("--"); m_Controls->m_ExclusionImageSelectionWidget->SetSelectionIsOptional(true); connect( m_TrackingTimer, SIGNAL(timeout()), this, SLOT(TimerUpdate()) ); connect( m_Controls->m_SaveParametersButton, SIGNAL(clicked()), this, SLOT(SaveParameters()) ); connect( m_Controls->commandLinkButton_2, SIGNAL(clicked()), this, SLOT(StopTractography()) ); connect( m_Controls->commandLinkButton, SIGNAL(clicked()), this, SLOT(DoFiberTracking()) ); connect( m_Controls->m_InteractiveBox, SIGNAL(stateChanged(int)), this, SLOT(ToggleInteractive()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(UpdateGui()) ); connect( m_Controls->m_FaImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(DeleteTrackingHandler()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(DeleteTrackingHandler()) ); connect( m_Controls->m_OutputProbMap, SIGNAL(stateChanged(int)), this, SLOT(OutputStyleSwitched()) ); connect( m_Controls->m_SeedImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ModeBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StopImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_TargetImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_PriorImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ExclusionImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MaskImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FaImageSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ForestSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(ForestSwitched()) ); connect( m_Controls->m_ForestSelectionWidget, SIGNAL(CurrentSelectionChanged(QList)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SeedsPerVoxelBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_NumFibersBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_ScalarThresholdBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_OdfCutoffBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StepSizeBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SamplingDistanceBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_AngularThresholdBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MinTractLengthBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_fBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_gBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_NumSamplesBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SeedRadiusBox, SIGNAL(editingFinished()), this, SLOT(InteractiveSeedChanged()) ); connect( m_Controls->m_NumSeedsBox, SIGNAL(editingFinished()), this, SLOT(InteractiveSeedChanged()) ); connect( m_Controls->m_OutputProbMap, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_SharpenOdfsBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_InterpolationBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_MaskInterpolationBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipXBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipYBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FlipZBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_FrontalSamplesBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_StopVotesBox, SIGNAL(stateChanged(int)), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_LoopCheckBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_TrialsPerSeedBox, SIGNAL(editingFinished()), this, SLOT(OnParameterChanged()) ); connect( m_Controls->m_EpConstraintsBox, SIGNAL(currentIndexChanged(int)), this, SLOT(OnParameterChanged()) ); m_Controls->m_SeedsPerVoxelBox->editingFinished(); m_Controls->m_NumFibersBox->editingFinished(); m_Controls->m_ScalarThresholdBox->editingFinished(); m_Controls->m_OdfCutoffBox->editingFinished(); m_Controls->m_StepSizeBox->editingFinished(); m_Controls->m_SamplingDistanceBox->editingFinished(); m_Controls->m_AngularThresholdBox->editingFinished(); m_Controls->m_MinTractLengthBox->editingFinished(); m_Controls->m_fBox->editingFinished(); m_Controls->m_gBox->editingFinished(); m_Controls->m_NumSamplesBox->editingFinished(); m_Controls->m_SeedRadiusBox->editingFinished(); m_Controls->m_NumSeedsBox->editingFinished(); m_Controls->m_LoopCheckBox->editingFinished(); m_Controls->m_TrialsPerSeedBox->editingFinished(); StartStopTrackingGui(false); } m_ParameterFile = QDir::currentPath()+"/param.stp"; UpdateGui(); } std::shared_ptr QmitkStreamlineTrackingView::GetParametersFromGui() { std::shared_ptr params = std::make_shared(); params->m_InteractiveRadius = m_Controls->m_SeedRadiusBox->value(); params->m_MaxNumFibers = m_Controls->m_NumFibersBox->value(); params->m_Cutoff = static_cast(m_Controls->m_ScalarThresholdBox->value()); params->m_F = static_cast(m_Controls->m_fBox->value()); params->m_G = static_cast(m_Controls->m_gBox->value()); params->m_OdfCutoff = static_cast(m_Controls->m_OdfCutoffBox->value()); params->m_SharpenOdfs = m_Controls->m_SharpenOdfsBox->isChecked(); params->m_Weight = static_cast(m_Controls->m_PriorWeightBox->value()); params->m_RestrictToPrior = m_Controls->m_PriorAsMaskBox->isChecked(); params->m_NewDirectionsFromPrior = m_Controls->m_NewDirectionsFromPriorBox->isChecked(); + params->m_PriorFlipX = m_Controls->m_PriorFlipXBox->isChecked(); + params->m_PriorFlipY = m_Controls->m_PriorFlipYBox->isChecked(); + params->m_PriorFlipZ = m_Controls->m_PriorFlipZBox->isChecked(); params->m_FlipX = m_Controls->m_FlipXBox->isChecked(); params->m_FlipY = m_Controls->m_FlipYBox->isChecked(); params->m_FlipZ = m_Controls->m_FlipZBox->isChecked(); params->m_InterpolateTractographyData = m_Controls->m_InterpolationBox->isChecked(); params->m_InterpolateRoiImages = m_Controls->m_MaskInterpolationBox->isChecked(); params->m_SeedsPerVoxel = m_Controls->m_SeedsPerVoxelBox->value(); params->SetStepSizeVox(m_Controls->m_StepSizeBox->value()); params->SetSamplingDistance(m_Controls->m_SamplingDistanceBox->value()); params->m_StopVotes = m_Controls->m_StopVotesBox->isChecked(); params->m_OnlyForwardSamples = m_Controls->m_FrontalSamplesBox->isChecked(); params->m_TrialsPerSeed = m_Controls->m_TrialsPerSeedBox->value(); params->m_NumSamples = m_Controls->m_NumSamplesBox->value(); params->SetLoopCheckDeg(m_Controls->m_LoopCheckBox->value()); params->SetAngularThresholdDeg(m_Controls->m_AngularThresholdBox->value()); params->m_MinTractLength = m_Controls->m_MinTractLengthBox->value(); params->m_OutputProbMap = m_Controls->m_OutputProbMap->isChecked(); params->m_FixRandomSeed = m_Controls->m_FixSeedBox->isChecked(); switch (m_Controls->m_ModeBox->currentIndex()) { case 0: params->m_Mode = mitk::TrackingDataHandler::MODE::DETERMINISTIC; break; case 1: params->m_Mode = mitk::TrackingDataHandler::MODE::PROBABILISTIC; break; default: params->m_Mode = mitk::TrackingDataHandler::MODE::DETERMINISTIC; } switch (m_Controls->m_EpConstraintsBox->currentIndex()) { case 0: params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::NONE; - m_Tracker->SetTargetRegions(nullptr); break; case 1: params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET; break; case 2: params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET_LABELDIFF; break; case 3: params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET; break; case 4: params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::MIN_ONE_EP_IN_TARGET; break; case 5: params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::ONE_EP_IN_TARGET; break; case 6: params->m_EpConstraints = itk::StreamlineTrackingFilter::EndpointConstraints::NO_EP_IN_TARGET; break; } return params; } void QmitkStreamlineTrackingView::SaveParameters() { QString filename = QFileDialog::getSaveFileName( 0, tr("Save Parameters"), m_ParameterFile, tr("Streamline Tractography Parameters (*.stp)") ); m_ParameterFile = filename; auto params = GetParametersFromGui(); params->SaveParameters(m_ParameterFile.toStdString()); } void QmitkStreamlineTrackingView::StopTractography() { if (m_Tracker.IsNull()) return; m_Tracker->SetStopTracking(true); } void QmitkStreamlineTrackingView::TimerUpdate() { if (m_Tracker.IsNull()) return; QString status_text(m_Tracker->GetStatusText().c_str()); m_Controls->m_StatusTextBox->setText(status_text); } void QmitkStreamlineTrackingView::BeforeThread() { m_TrackingTimer->start(1000); } void QmitkStreamlineTrackingView::AfterThread() { auto params = m_Tracker->GetParameters(); m_TrackingTimer->stop(); if (!params->m_OutputProbMap) { vtkSmartPointer fiberBundle = m_Tracker->GetFiberPolyData(); if (!m_Controls->m_InteractiveBox->isChecked() && fiberBundle->GetNumberOfLines() == 0) { QMessageBox warnBox; warnBox.setWindowTitle("Warning"); warnBox.setText("No fiberbundle was generated!"); warnBox.setDetailedText("No fibers were generated using the chosen parameters. Typical reasons are:\n\n- Cutoff too high. Some images feature very low FA/GFA/peak size. Try to lower this parameter.\n- Angular threshold too strict. Try to increase this parameter.\n- A small step sizes also means many steps to go wrong. Especially in the case of probabilistic tractography. Try to adjust the angular threshold."); warnBox.setIcon(QMessageBox::Warning); warnBox.exec(); if (m_InteractivePointSetNode.IsNotNull()) m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); StartStopTrackingGui(false); if (m_DeleteTrackingHandler) DeleteTrackingHandler(); UpdateGui(); return; } mitk::FiberBundle::Pointer fib = mitk::FiberBundle::New(fiberBundle); fib->SetReferenceGeometry(dynamic_cast(m_ParentNode->GetData())->GetGeometry()); if (m_Controls->m_ResampleFibersBox->isChecked() && fiberBundle->GetNumberOfLines()>0) fib->Compress(m_Controls->m_FiberErrorBox->value()); fib->ColorFibersByOrientation(); m_Tracker->SetDicomProperties(fib); if (m_Controls->m_InteractiveBox->isChecked()) { if (m_InteractiveNode.IsNull()) { m_InteractiveNode = mitk::DataNode::New(); QString name("Interactive"); m_InteractiveNode->SetName(name.toStdString()); GetDataStorage()->Add(m_InteractiveNode); } m_InteractiveNode->SetData(fib); m_InteractiveNode->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); if (auto renderWindowPart = this->GetRenderWindowPart()) renderWindowPart->RequestUpdate(); } else { mitk::DataNode::Pointer node = mitk::DataNode::New(); node->SetData(fib); QString name("FiberBundle_"); name += m_ParentNode->GetName().c_str(); name += "_Streamline"; node->SetName(name.toStdString()); node->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); GetDataStorage()->Add(node, m_ParentNode); } } else { TrackerType::ItkDoubleImgType::Pointer outImg = m_Tracker->GetOutputProbabilityMap(); mitk::Image::Pointer img = mitk::Image::New(); img->InitializeByItk(outImg.GetPointer()); img->SetVolume(outImg->GetBufferPointer()); if (m_Controls->m_InteractiveBox->isChecked()) { if (m_InteractiveNode.IsNull()) { m_InteractiveNode = mitk::DataNode::New(); QString name("Interactive"); m_InteractiveNode->SetName(name.toStdString()); GetDataStorage()->Add(m_InteractiveNode); } m_InteractiveNode->SetData(img); mitk::LookupTable::Pointer lut = mitk::LookupTable::New(); lut->SetType(mitk::LookupTable::JET_TRANSPARENT); mitk::LookupTableProperty::Pointer lut_prop = mitk::LookupTableProperty::New(); lut_prop->SetLookupTable(lut); m_InteractiveNode->SetProperty("LookupTable", lut_prop); m_InteractiveNode->SetProperty("opacity", mitk::FloatProperty::New(0.5)); m_InteractiveNode->SetFloatProperty("Fiber2DSliceThickness", m_Tracker->GetMinVoxelSize()/2); if (auto renderWindowPart = this->GetRenderWindowPart()) renderWindowPart->RequestUpdate(); } else { mitk::DataNode::Pointer node = mitk::DataNode::New(); node->SetData(img); QString name("ProbabilityMap_"); name += m_ParentNode->GetName().c_str(); node->SetName(name.toStdString()); mitk::LookupTable::Pointer lut = mitk::LookupTable::New(); lut->SetType(mitk::LookupTable::JET_TRANSPARENT); mitk::LookupTableProperty::Pointer lut_prop = mitk::LookupTableProperty::New(); lut_prop->SetLookupTable(lut); node->SetProperty("LookupTable", lut_prop); node->SetProperty("opacity", mitk::FloatProperty::New(0.5)); GetDataStorage()->Add(node, m_ParentNode); } } if (m_InteractivePointSetNode.IsNotNull()) m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); StartStopTrackingGui(false); if (m_DeleteTrackingHandler) DeleteTrackingHandler(); UpdateGui(); } void QmitkStreamlineTrackingView::InteractiveSeedChanged(bool posChanged) { if(!CheckAndStoreLastParams(sender()) && !posChanged) return; if (m_ThreadIsRunning || !m_Visible) return; if (!posChanged && (!m_Controls->m_InteractiveBox->isChecked() || !m_Controls->m_ParamUpdateBox->isChecked()) ) return; std::srand(std::time(0)); m_SeedPoints.clear(); itk::Point world_pos = this->GetRenderWindowPart()->GetSelectedPosition(); m_SeedPoints.push_back(world_pos); float radius = m_Controls->m_SeedRadiusBox->value(); int num = m_Controls->m_NumSeedsBox->value(); mitk::PointSet::Pointer pointset = mitk::PointSet::New(); pointset->InsertPoint(0, world_pos); m_InteractivePointSetNode->SetProperty("pointsize", mitk::FloatProperty::New(radius*2)); m_InteractivePointSetNode->SetProperty("point 2D size", mitk::FloatProperty::New(radius*2)); m_InteractivePointSetNode->SetData(pointset); for (int i=1; i p; p[0] = rand()%1000-500; p[1] = rand()%1000-500; p[2] = rand()%1000-500; p.Normalize(); p *= radius; m_SeedPoints.push_back(world_pos+p); } m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,0,0)); DoFiberTracking(); } bool QmitkStreamlineTrackingView::CheckAndStoreLastParams(QObject* obj) { if (obj!=nullptr) { std::string new_val = ""; if(qobject_cast(obj)!=nullptr) new_val = boost::lexical_cast(qobject_cast(obj)->value()); else if (qobject_cast(obj)!=nullptr) new_val = boost::lexical_cast(qobject_cast(obj)->value()); else return true; if (m_LastTractoParams.find(obj->objectName())==m_LastTractoParams.end()) { m_LastTractoParams[obj->objectName()] = new_val; return false; } else if (m_LastTractoParams.at(obj->objectName()) != new_val) { m_LastTractoParams[obj->objectName()] = new_val; return true; } else if (m_LastTractoParams.at(obj->objectName()) == new_val) return false; } return true; } void QmitkStreamlineTrackingView::OnParameterChanged() { UpdateGui(); if(!CheckAndStoreLastParams(sender())) return; if (m_Controls->m_InteractiveBox->isChecked() && m_Controls->m_ParamUpdateBox->isChecked()) DoFiberTracking(); } void QmitkStreamlineTrackingView::ToggleInteractive() { UpdateGui(); m_Controls->m_SeedsPerVoxelBox->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->m_SeedsPerVoxelLabel->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->m_SeedImageSelectionWidget->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); m_Controls->label_6->setEnabled(!m_Controls->m_InteractiveBox->isChecked()); if ( m_Controls->m_InteractiveBox->isChecked() ) { if (m_FirstInteractiveRun) { QMessageBox::information(nullptr, "Information", "Place and move a spherical seed region anywhere in the image by left-clicking and dragging. If the seed region is colored red, tracking is in progress. If the seed region is colored white, tracking is finished.\nPlacing the seed region for the first time in a newly selected dataset might cause a short delay, since the tracker needs to be initialized."); m_FirstInteractiveRun = false; } QApplication::setOverrideCursor(Qt::PointingHandCursor); QApplication::processEvents(); m_InteractivePointSetNode = mitk::DataNode::New(); m_InteractivePointSetNode->SetProperty("color", mitk::ColorProperty::New(1,1,1)); m_InteractivePointSetNode->SetName("InteractiveSeedRegion"); mitk::PointSetShapeProperty::Pointer shape_prop = mitk::PointSetShapeProperty::New(); shape_prop->SetValue(mitk::PointSetShapeProperty::PointSetShape::CIRCLE); m_InteractivePointSetNode->SetProperty("Pointset.2D.shape", shape_prop); GetDataStorage()->Add(m_InteractivePointSetNode); m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); connect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); } else { QApplication::restoreOverrideCursor(); QApplication::processEvents(); m_InteractiveNode = nullptr; m_InteractivePointSetNode = nullptr; m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); disconnect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); } } void QmitkStreamlineTrackingView::Activated() { } void QmitkStreamlineTrackingView::Deactivated() { } void QmitkStreamlineTrackingView::Visible() { m_Visible = true; } void QmitkStreamlineTrackingView::Hidden() { m_Visible = false; m_Controls->m_InteractiveBox->setChecked(false); ToggleInteractive(); } void QmitkStreamlineTrackingView::OnSliceChanged() { InteractiveSeedChanged(true); } void QmitkStreamlineTrackingView::SetFocus() { } void QmitkStreamlineTrackingView::DeleteTrackingHandler() { if (!m_ThreadIsRunning && m_TrackingHandler != nullptr) { if (m_TrackingPriorHandler != nullptr) { delete m_TrackingPriorHandler; m_TrackingPriorHandler = nullptr; } delete m_TrackingHandler; m_TrackingHandler = nullptr; m_DeleteTrackingHandler = false; m_LastPrior = nullptr; } else if (m_ThreadIsRunning) { m_DeleteTrackingHandler = true; } } void QmitkStreamlineTrackingView::ForestSwitched() { DeleteTrackingHandler(); } void QmitkStreamlineTrackingView::OutputStyleSwitched() { if (m_InteractiveNode.IsNotNull()) GetDataStorage()->Remove(m_InteractiveNode); m_InteractiveNode = nullptr; } void QmitkStreamlineTrackingView::OnSelectionChanged( berry::IWorkbenchPart::Pointer , const QList& nodes ) { std::vector< mitk::DataNode::Pointer > last_nodes = m_InputImageNodes; m_InputImageNodes.clear(); m_AdditionalInputImages.clear(); bool retrack = false; for( auto node : nodes ) { if( node.IsNotNull() && dynamic_cast(node->GetData()) ) { if( dynamic_cast(node->GetData()) || dynamic_cast(node->GetData()) || dynamic_cast(node->GetData()) || dynamic_cast(node->GetData()) || mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(node->GetData()))) { m_InputImageNodes.push_back(node); retrack = true; } else { mitk::Image* img = dynamic_cast(node->GetData()); if (img!=nullptr && img->GetDimension()==3) m_AdditionalInputImages.push_back(dynamic_cast(node->GetData())); } } } // sometimes the OnSelectionChanged event is sent twice and actually no selection has changed for the first event. We need to catch that. if (last_nodes.size() == m_InputImageNodes.size()) { bool same_nodes = true; for (unsigned int i=0; im_TensorImageLabel->setText("select in data-manager"); m_Controls->m_fBox->setEnabled(false); m_Controls->m_fLabel->setEnabled(false); m_Controls->m_gBox->setEnabled(false); m_Controls->m_gLabel->setEnabled(false); m_Controls->m_FaImageSelectionWidget->setEnabled(true); m_Controls->mFaImageLabel->setEnabled(true); m_Controls->m_OdfCutoffBox->setEnabled(false); m_Controls->m_OdfCutoffLabel->setEnabled(false); m_Controls->m_SharpenOdfsBox->setEnabled(false); m_Controls->m_ForestSelectionWidget->setVisible(false); m_Controls->m_ForestLabel->setVisible(false); m_Controls->commandLinkButton->setEnabled(false); m_Controls->m_TrialsPerSeedBox->setEnabled(false); m_Controls->m_TrialsPerSeedLabel->setEnabled(false); m_Controls->m_TargetImageSelectionWidget->setEnabled(false); m_Controls->m_TargetImageLabel->setEnabled(false); if (m_Controls->m_InteractiveBox->isChecked()) { m_Controls->m_InteractiveSeedingFrame->setVisible(true); m_Controls->m_StaticSeedingFrame->setVisible(false); m_Controls->commandLinkButton_2->setVisible(false); m_Controls->commandLinkButton->setVisible(false); } else { m_Controls->m_InteractiveSeedingFrame->setVisible(false); m_Controls->m_StaticSeedingFrame->setVisible(true); m_Controls->commandLinkButton_2->setVisible(m_ThreadIsRunning); m_Controls->commandLinkButton->setVisible(!m_ThreadIsRunning); } if (m_Controls->m_EpConstraintsBox->currentIndex()>0) { m_Controls->m_TargetImageSelectionWidget->setEnabled(true); m_Controls->m_TargetImageLabel->setEnabled(true); } // trials per seed are only important for probabilistic tractography if (m_Controls->m_ModeBox->currentIndex()==1) { m_Controls->m_TrialsPerSeedBox->setEnabled(true); m_Controls->m_TrialsPerSeedLabel->setEnabled(true); } if(!m_InputImageNodes.empty()) { if (m_InputImageNodes.size()>1) m_Controls->m_TensorImageLabel->setText( ( std::to_string(m_InputImageNodes.size()) + " images selected").c_str() ); else m_Controls->m_TensorImageLabel->setText(m_InputImageNodes.at(0)->GetName().c_str()); m_Controls->commandLinkButton->setEnabled(!m_Controls->m_InteractiveBox->isChecked() && !m_ThreadIsRunning); m_Controls->m_ScalarThresholdBox->setEnabled(true); m_Controls->m_FaThresholdLabel->setEnabled(true); if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { m_Controls->m_fBox->setEnabled(true); m_Controls->m_fLabel->setEnabled(true); m_Controls->m_gBox->setEnabled(true); m_Controls->m_gLabel->setEnabled(true); } else if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) || dynamic_cast(m_InputImageNodes.at(0)->GetData())) { m_Controls->m_OdfCutoffBox->setEnabled(true); m_Controls->m_OdfCutoffLabel->setEnabled(true); m_Controls->m_SharpenOdfsBox->setEnabled(true); } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(m_InputImageNodes.at(0)->GetData())) ) { m_Controls->m_ForestSelectionWidget->setVisible(true); m_Controls->m_ForestLabel->setVisible(true); m_Controls->m_ScalarThresholdBox->setEnabled(false); m_Controls->m_FaThresholdLabel->setEnabled(false); } } } void QmitkStreamlineTrackingView::StartStopTrackingGui(bool start) { m_ThreadIsRunning = start; if (!m_Controls->m_InteractiveBox->isChecked()) { m_Controls->commandLinkButton_2->setVisible(start); m_Controls->commandLinkButton->setVisible(!start); m_Controls->m_InteractiveBox->setEnabled(!start); m_Controls->m_StatusTextBox->setVisible(start); } } void QmitkStreamlineTrackingView::DoFiberTracking() { auto params = GetParametersFromGui(); if (m_InputImageNodes.empty()) { QMessageBox::information(nullptr, "Information", "Please select an input image in the datamaneger (tensor, ODF, peak or dMRI image)!"); return; } if (m_ThreadIsRunning || !m_Visible) return; if (m_Controls->m_InteractiveBox->isChecked() && m_SeedPoints.empty()) return; StartStopTrackingGui(true); m_Tracker = TrackerType::New(); + if (params->m_EpConstraints == itk::StreamlineTrackingFilter::EndpointConstraints::NONE) + m_Tracker->SetTargetRegions(nullptr); + if( dynamic_cast(m_InputImageNodes.at(0)->GetData()) ) { if (m_Controls->m_ModeBox->currentIndex()==1) { if (m_InputImageNodes.size()>1) { QMessageBox::information(nullptr, "Information", "Probabilistic tensor tractography is only implemented for single-tensor mode!"); StartStopTrackingGui(false); return; } if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerOdf(); typedef itk::TensorImageToOdfImageFilter< float, float > FilterType; FilterType::Pointer filter = FilterType::New(); filter->SetInput( mitk::convert::GetItkTensorFromTensorImage(dynamic_cast(m_InputImageNodes.at(0)->GetData())) ); filter->Update(); dynamic_cast(m_TrackingHandler)->SetOdfImage(filter->GetOutput()); if (m_Controls->m_FaImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageSelectionWidget->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetGfaImage(itkImg); } } dynamic_cast(m_TrackingHandler)->SetIsOdfFromTensor(true); } else { if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerTensor(); for (unsigned int i=0; i(m_TrackingHandler)->AddTensorImage(mitk::convert::GetItkTensorFromTensorImage(dynamic_cast(m_InputImageNodes.at(i)->GetData())).GetPointer()); if (m_Controls->m_FaImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageSelectionWidget->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetFaImage(itkImg); } } } } else if ( dynamic_cast(m_InputImageNodes.at(0)->GetData()) || dynamic_cast(m_InputImageNodes.at(0)->GetData())) { if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerOdf(); if (dynamic_cast(m_InputImageNodes.at(0)->GetData())) dynamic_cast(m_TrackingHandler)->SetOdfImage(mitk::convert::GetItkOdfFromShImage(dynamic_cast(m_InputImageNodes.at(0)->GetData()))); else dynamic_cast(m_TrackingHandler)->SetOdfImage(mitk::convert::GetItkOdfFromOdfImage(dynamic_cast(m_InputImageNodes.at(0)->GetData()))); if (m_Controls->m_FaImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer itkImg = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_FaImageSelectionWidget->GetSelectedNode()->GetData()), itkImg); dynamic_cast(m_TrackingHandler)->SetGfaImage(itkImg); } } } else if ( mitk::DiffusionPropertyHelper::IsDiffusionWeightedImage( dynamic_cast(m_InputImageNodes.at(0)->GetData())) ) { if ( m_Controls->m_ForestSelectionWidget->GetSelectedNode().IsNull() ) { QMessageBox::information(nullptr, "Information", "Not random forest for machine learning based tractography (raw dMRI tractography) selected. Did you accidentally select the raw diffusion-weighted image in the datamanager?"); StartStopTrackingGui(false); return; } if (m_TrackingHandler==nullptr) { mitk::TractographyForest::Pointer forest = dynamic_cast(m_Controls->m_ForestSelectionWidget->GetSelectedNode()->GetData()); mitk::Image::Pointer dwi = dynamic_cast(m_InputImageNodes.at(0)->GetData()); std::vector< std::vector< ItkFloatImageType::Pointer > > additionalFeatureImages; additionalFeatureImages.push_back(std::vector< ItkFloatImageType::Pointer >()); for (auto img : m_AdditionalInputImages) { ItkFloatImageType::Pointer itkimg = ItkFloatImageType::New(); mitk::CastToItkImage(img, itkimg); additionalFeatureImages.at(0).push_back(itkimg); } bool forest_valid = false; if (forest->GetNumFeatures()>=100) { params->m_NumPreviousDirections = static_cast((forest->GetNumFeatures() - (100 + additionalFeatureImages.at(0).size()))/3); m_TrackingHandler = new mitk::TrackingHandlerRandomForest<6, 100>(); dynamic_cast*>(m_TrackingHandler)->AddDwi(dwi); dynamic_cast*>(m_TrackingHandler)->SetAdditionalFeatureImages(additionalFeatureImages); dynamic_cast*>(m_TrackingHandler)->SetForest(forest); forest_valid = dynamic_cast*>(m_TrackingHandler)->IsForestValid(); } else { params->m_NumPreviousDirections = static_cast((forest->GetNumFeatures() - (28 + additionalFeatureImages.at(0).size()))/3); m_TrackingHandler = new mitk::TrackingHandlerRandomForest<6, 28>(); dynamic_cast*>(m_TrackingHandler)->AddDwi(dwi); dynamic_cast*>(m_TrackingHandler)->SetAdditionalFeatureImages(additionalFeatureImages); dynamic_cast*>(m_TrackingHandler)->SetForest(forest); forest_valid = dynamic_cast*>(m_TrackingHandler)->IsForestValid(); } if (!forest_valid) { QMessageBox::information(nullptr, "Information", "Random forest is invalid. The forest signatue does not match the parameters of TrackingHandlerRandomForest."); StartStopTrackingGui(false); return; } } } else { if (m_Controls->m_ModeBox->currentIndex()==1) { QMessageBox::information(nullptr, "Information", "Probabilstic tractography is not implemented for peak images."); StartStopTrackingGui(false); return; } if (m_TrackingHandler==nullptr) { m_TrackingHandler = new mitk::TrackingHandlerPeaks(); dynamic_cast(m_TrackingHandler)->SetPeakImage(mitk::convert::GetItkPeakFromPeakImage(dynamic_cast(m_InputImageNodes.at(0)->GetData()))); } } if (m_Controls->m_InteractiveBox->isChecked()) { m_Tracker->SetSeedPoints(m_SeedPoints); } else if (m_Controls->m_SeedImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_SeedImageSelectionWidget->GetSelectedNode()->GetData()), mask); m_Tracker->SetSeedImage(mask); } if (m_Controls->m_MaskImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_MaskImageSelectionWidget->GetSelectedNode()->GetData()), mask); m_Tracker->SetMaskImage(mask); } if (m_Controls->m_StopImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_StopImageSelectionWidget->GetSelectedNode()->GetData()), mask); m_Tracker->SetStoppingRegions(mask); } if (m_Controls->m_TargetImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_TargetImageSelectionWidget->GetSelectedNode()->GetData()), mask); m_Tracker->SetTargetRegions(mask); } if (m_Controls->m_PriorImageSelectionWidget->GetSelectedNode().IsNotNull()) { auto prior_params = GetParametersFromGui(); if (m_LastPrior!=m_Controls->m_PriorImageSelectionWidget->GetSelectedNode() || m_TrackingPriorHandler==nullptr) { typedef mitk::ImageToItk< mitk::TrackingHandlerPeaks::PeakImgType > CasterType; CasterType::Pointer caster = CasterType::New(); caster->SetInput(dynamic_cast(m_Controls->m_PriorImageSelectionWidget->GetSelectedNode()->GetData())); caster->SetCopyMemFlag(true); caster->Update(); mitk::TrackingHandlerPeaks::PeakImgType::Pointer itkImg = caster->GetOutput(); m_TrackingPriorHandler = new mitk::TrackingHandlerPeaks(); dynamic_cast(m_TrackingPriorHandler)->SetPeakImage(itkImg); m_LastPrior = m_Controls->m_PriorImageSelectionWidget->GetSelectedNode(); } - switch (m_Controls->m_ModeBox->currentIndex()) - { - case 0: - m_TrackingPriorHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); - break; - case 1: - m_TrackingPriorHandler->SetMode(mitk::TrackingDataHandler::MODE::PROBABILISTIC); - break; - default: - m_TrackingPriorHandler->SetMode(mitk::TrackingDataHandler::MODE::DETERMINISTIC); - } - m_TrackingPriorHandler->SetFlipX(m_Controls->m_PriorFlipXBox->isChecked()); - m_TrackingPriorHandler->SetFlipY(m_Controls->m_PriorFlipYBox->isChecked()); - m_TrackingPriorHandler->SetFlipZ(m_Controls->m_PriorFlipZBox->isChecked()); + + prior_params->m_FlipX = m_Controls->m_PriorFlipXBox->isChecked(); + prior_params->m_FlipY = m_Controls->m_PriorFlipYBox->isChecked(); + prior_params->m_FlipZ = m_Controls->m_PriorFlipZBox->isChecked(); m_TrackingPriorHandler->SetParameters(prior_params); m_Tracker->SetTrackingPriorHandler(m_TrackingPriorHandler); } else if (m_Controls->m_PriorImageSelectionWidget->GetSelectedNode().IsNull()) m_Tracker->SetTrackingPriorHandler(nullptr); if (m_Controls->m_ExclusionImageSelectionWidget->GetSelectedNode().IsNotNull()) { ItkFloatImageType::Pointer mask = ItkFloatImageType::New(); mitk::CastToItkImage(dynamic_cast(m_Controls->m_ExclusionImageSelectionWidget->GetSelectedNode()->GetData()), mask); m_Tracker->SetExclusionRegions(mask); } - if (params->m_EpConstraints == itk::StreamlineTrackingFilter::EndpointConstraints::NONE) - m_Tracker->SetTargetRegions(nullptr); - break; - case 1: - m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET); - break; - case 2: - m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_TARGET_LABELDIFF); - break; - case 3: - m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET); - break; - case 4: - m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::MIN_ONE_EP_IN_TARGET); - break; - case 5: - m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::ONE_EP_IN_TARGET); - break; - case 6: - m_Tracker->SetEndpointConstraint(itk::StreamlineTrackingFilter::EndpointConstraints::NO_EP_IN_TARGET); - break; - } - if (params->m_EpConstraints!=itk::StreamlineTrackingFilter::EndpointConstraints::NONE && m_Controls->m_TargetImageSelectionWidget->GetSelectedNode().IsNull()) { QMessageBox::information(nullptr, "Error", "Endpoint constraints are used but no target image is set!"); StartStopTrackingGui(false); return; } else if (params->m_EpConstraints==itk::StreamlineTrackingFilter::EndpointConstraints::EPS_IN_SEED_AND_TARGET && (m_Controls->m_SeedImageSelectionWidget->GetSelectedNode().IsNull()|| m_Controls->m_TargetImageSelectionWidget->GetSelectedNode().IsNull()) ) { QMessageBox::information(nullptr, "Error", "Endpoint constraint EPS_IN_SEED_AND_TARGET is used but no target or no seed image is set!"); StartStopTrackingGui(false); return; } + m_Tracker->SetParameters(params); m_Tracker->SetTrackingHandler(m_TrackingHandler); m_Tracker->SetVerbose(!m_Controls->m_InteractiveBox->isChecked()); m_ParentNode = m_InputImageNodes.at(0); m_TrackingThread.start(QThread::LowestPriority); }