diff --git a/Plugins/org.mitk.gui.qt.bonesegmentation/resources/boneseg.qrc b/Plugins/org.mitk.gui.qt.bonesegmentation/resources/boneseg.qrc index ca8a3ae4e7..53910ba52f 100644 --- a/Plugins/org.mitk.gui.qt.bonesegmentation/resources/boneseg.qrc +++ b/Plugins/org.mitk.gui.qt.bonesegmentation/resources/boneseg.qrc @@ -1,5 +1,6 @@ segment.py + trained_bone_seg_unet.pth.tar \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.bonesegmentation/resources/segment.py b/Plugins/org.mitk.gui.qt.bonesegmentation/resources/segment.py index fae06239d0..7675fd7f8d 100644 --- a/Plugins/org.mitk.gui.qt.bonesegmentation/resources/segment.py +++ b/Plugins/org.mitk.gui.qt.bonesegmentation/resources/segment.py @@ -1,379 +1,374 @@ import os import numpy as np import SimpleITK import torch import torch.cuda print('Started python script...') -f = open('/tmp/file.txt', 'w') -f.close() - -import os import SimpleITK as sitk -import numpy as np from skimage.transform import resize import sys def resize_image(image, old_spacing, new_spacing, order=3): new_shape = (int(np.round(old_spacing[0]/new_spacing[0]*float(image.shape[0]))), int(np.round(old_spacing[1]/new_spacing[1]*float(image.shape[1]))), int(np.round(old_spacing[2]/new_spacing[2]*float(image.shape[2])))) return resize(image, new_shape, order=order, mode='edge') def cut_off_values_upper_lower_percentile(image, mask=None, percentile_lower=0.2, percentile_upper=99.8): if mask is None: mask = image!=image[0,0,0] cut_off_lower = np.percentile(image[mask!=0].ravel(), percentile_lower) cut_off_upper = np.percentile(image[mask!=0].ravel(), percentile_upper) res = np.copy(image) res[(res < cut_off_lower) & (mask !=0 )] = cut_off_lower res[(res > cut_off_upper) & (mask !=0 )] = cut_off_upper return image def preprocess_image(itk_image, is_seg=False, spacing_target=(1, 0.5, 0.5)): spacing = np.array(itk_image.GetSpacing())[[2, 1, 0]] image = sitk.GetArrayFromImage(itk_image).astype(float) if not is_seg: image = resize_image(image, spacing, spacing_target).astype(np.float32) # cut off outliers image = cut_off_values_upper_lower_percentile(image, np.ones(image.shape), 1., 99.) #subtract mean, divide by std. use heuristic masking image -= image.mean() image /= image.std() else: image = resize_image(image, spacing, spacing_target, 0) return image def load_and_preprocess(in_image): images = {} np_array = sitk.GetArrayFromImage(in_image).astype(float) if len(np_array.shape) > 3: b0 = sitk.GetImageFromArray(np_array[:,:,:,0]) b0.SetSpacing(in_image.GetSpacing()) b0.SetOrigin(in_image.GetOrigin()) b0.SetDirection(in_image.GetDirection()) images["T1"] = b0 elif len(np_array.shape)==3 : images["T1"] = in_image properties_dict = { "spacing": in_image.GetSpacing(), "direction": in_image.GetDirection(), "size": in_image.GetSize(), "origin": in_image.GetOrigin() } for k in images.keys(): images[k] = preprocess_image(images[k], is_seg=False, spacing_target=(1.5, 1.5, 1.5)) properties_dict['size_before_cropping'] = images["T1"].shape imgs = [] for seq in ['T1']: imgs.append(images[seq][None]) all_data = np.vstack(imgs) return all_data, properties_dict def get_sitk_from_nparray(segmentation, original_image, dct) : ''' segmentation must have the same spacing as the original nifti (for now). segmentation may have been cropped out of the original image :param segmentation: :param dct: :return: ''' old_size = np.array(dct['size_before_cropping']) bbox = dct.get('brain_bbox') if bbox is not None: seg_old_size = np.zeros(old_size) for c in range(3): bbox[c][1] = np.min((bbox[c][0] + segmentation.shape[c], old_size[c])) seg_old_size[bbox[0][0]:bbox[0][1], bbox[1][0]:bbox[1][1], bbox[2][0]:bbox[2][1]] = segmentation else: seg_old_size = segmentation seg_old_spacing = resize_segmentation(seg_old_size, np.array(dct['size'])[[2, 1, 0]], order=3) seg_resized_itk = sitk.GetImageFromArray(seg_old_spacing.astype(np.uint8)) seg_resized_itk.SetSpacing(np.array(dct['spacing'])[[0, 1, 2]]) seg_resized_itk.SetOrigin(dct['origin']) seg_resized_itk.SetDirection(dct['direction']) if original_image is not None : image = sitk.GetArrayFromImage(original_image).astype(float) if len(image.shape) > 3 : for i in range(image.shape[3]) : image[:,:,:,i] *= seg_old_spacing else : image *= seg_old_spacing brain_extracted = sitk.GetImageFromArray(image.astype(np.float32)) brain_extracted.SetSpacing(np.array(dct['spacing'])[[0, 1, 2]]) brain_extracted.SetOrigin(dct['origin']) brain_extracted.SetDirection(dct['direction']) else : brain_extracted = None return seg_resized_itk, brain_extracted def save_segmentation_nifti(segmentation, dct, out_fname): ''' segmentation must have the same spacing as the original nifti (for now). segmentation may have been cropped out of the original image :param segmentation: :param dct: :param out_fname: :return: ''' old_size = np.array(dct['size_before_cropping']) bbox = dct.get('brain_bbox') if bbox is not None: seg_old_size = np.zeros(old_size) for c in range(3): bbox[c][1] = np.min((bbox[c][0] + segmentation.shape[c], old_size[c])) seg_old_size[bbox[0][0]:bbox[0][1], bbox[1][0]:bbox[1][1], bbox[2][0]:bbox[2][1]] = segmentation else: seg_old_size = segmentation seg_old_spacing = resize_segmentation(seg_old_size, np.array(dct['size'])[[2, 1, 0]], order=3) seg_resized_itk = sitk.GetImageFromArray(seg_old_spacing.astype(np.uint8)) seg_resized_itk.SetSpacing(np.array(dct['spacing'])[[0, 1, 2]]) seg_resized_itk.SetOrigin(dct['origin']) seg_resized_itk.SetDirection(dct['direction']) sitk.WriteImage(seg_resized_itk, out_fname) def resize_segmentation(segmentation, new_shape, order=3): unique_labels = np.unique(segmentation) assert len(segmentation.shape) == len(new_shape), "new shape must have same dimensionality as segmentation" reshaped_multihot = np.zeros([len(unique_labels)] + list(new_shape), dtype=float) for i, c in enumerate(unique_labels): reshaped_multihot[i] = resize((segmentation == c).astype(float), new_shape, order, mode="constant", cval=0, clip=True) reshaped = unique_labels[np.argmax(reshaped_multihot, 0)] return reshaped.astype(segmentation.dtype) #!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright 2017 Division of Medical Image Computing, German Cancer Research Center (DKFZ) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np def reshape(orig_img, append_value=-1024, new_shape=(512, 512, 512)): reshaped_image = np.zeros(new_shape) reshaped_image[...] = append_value x_offset = 0 y_offset = 0 # (new_shape[1] - orig_img.shape[1]) // 2 z_offset = 0 # (new_shape[2] - orig_img.shape[2]) // 2 reshaped_image[x_offset:orig_img.shape[0]+x_offset, y_offset:orig_img.shape[1]+y_offset, z_offset:orig_img.shape[2]+z_offset] = orig_img # insert temp_img.min() as background value return reshaped_image def crop_image_to_orig_size(image, orig_shape): x_offset = 0 y_offset = 0 # (512 - orig_shape[1]) // 2 z_offset = 0 # (512 - orig_shape[2]) // 2 return image[x_offset:orig_shape[0] + x_offset, y_offset:orig_shape[1] + y_offset, z_offset:orig_shape[2] + z_offset] #!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright 2017 Division of Medical Image Computing, German Cancer Research Center (DKFZ) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Defines the Unet. # |num_downs|: number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 at the bottleneck # recursive implementation of Unet import torch from torch import nn class UNet(nn.Module): def __init__(self, num_classes=3, in_channels=1, initial_filter_size=64, kernel_size=3, num_downs=4, norm_layer=nn.InstanceNorm2d): # norm_layer=nn.BatchNorm2d, use_dropout=False): super(UNet, self).__init__() # construct unet structure unet_block = UnetSkipConnectionBlock(in_channels=initial_filter_size * 2 ** (num_downs-1), out_channels=initial_filter_size * 2 ** num_downs, num_classes=num_classes, kernel_size=kernel_size, norm_layer=norm_layer, innermost=True) for i in range(1, num_downs): unet_block = UnetSkipConnectionBlock(in_channels=initial_filter_size * 2 ** (num_downs-(i+1)), out_channels=initial_filter_size * 2 ** (num_downs-i), num_classes=num_classes, kernel_size=kernel_size, submodule=unet_block, norm_layer=norm_layer) unet_block = UnetSkipConnectionBlock(in_channels=in_channels, out_channels=initial_filter_size, num_classes=num_classes, kernel_size=kernel_size, submodule=unet_block, norm_layer=norm_layer, outermost=True) self.model = unet_block def forward(self, x): return self.model(x) # Defines the submodule with skip connection. # X -------------------identity---------------------- X # |-- downsampling -- |submodule| -- upsampling --| class UnetSkipConnectionBlock(nn.Module): def __init__(self, in_channels=None, out_channels=None, num_classes=1, kernel_size=3, submodule=None, outermost=False, innermost=False, norm_layer=nn.InstanceNorm2d, use_dropout=False): super(UnetSkipConnectionBlock, self).__init__() self.outermost = outermost # downconv pool = nn.MaxPool2d(2, stride=2) conv1 = self.contract(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, norm_layer=norm_layer) conv2 = self.contract(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, norm_layer=norm_layer) # upconv conv3 = self.expand(in_channels=out_channels*2, out_channels=out_channels, kernel_size=kernel_size) conv4 = self.expand(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size) if outermost: final = nn.Conv2d(out_channels, num_classes, kernel_size=1) down = [conv1, conv2] up = [conv3, conv4, final] model = down + [submodule] + up elif innermost: upconv = nn.ConvTranspose2d(in_channels*2, in_channels, kernel_size=2, stride=2) model = [pool, conv1, conv2, upconv] else: upconv = nn.ConvTranspose2d(in_channels*2, in_channels, kernel_size=2, stride=2) down = [pool, conv1, conv2] up = [conv3, conv4, upconv] if use_dropout: model = down + [submodule] + up + [nn.Dropout(0.5)] else: model = down + [submodule] + up self.model = nn.Sequential(*model) @staticmethod def contract(in_channels, out_channels, kernel_size=3, norm_layer=nn.InstanceNorm2d): layer = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size, padding=1), norm_layer(out_channels), nn.LeakyReLU(inplace=True)) return layer @staticmethod def expand(in_channels, out_channels, kernel_size=3): layer = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size, padding=1), nn.LeakyReLU(inplace=True), ) return layer @staticmethod def center_crop(layer, target_width, target_height): batch_size, n_channels, layer_width, layer_height = layer.size() xy1 = (layer_width - target_width) // 2 xy2 = (layer_height - target_height) // 2 return layer[:, :, xy1:(xy1 + target_width), xy2:(xy2 + target_height)] def forward(self, x): if self.outermost: return self.model(x) else: crop = self.center_crop(self.model(x), x.size()[2], x.size()[3]) return torch.cat([x, crop], 1) batch_size = 8 num_classes = 2 dir_path = os.path.dirname(os.path.realpath(__file__)) seg_load_network_path = os.path.join(dir_path, 'trained_bone_seg_unet.pth.tar') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # print(sys.argv) model = UNet(num_classes=num_classes, in_channels=1) model.load_state_dict(torch.load( seg_load_network_path, map_location=device)) print(device) nrrd_image = SimpleITK.ReadImage(os.path.join(dir_path, 'temp_CT_1.nrrd')) # nrrd_image = SimpleITK.ReadImage(sys.argv[1]) model.eval() model.to(device) result = [] first = True print('Segmenting...') # nrrd_image = in_image input_image_array = SimpleITK.GetArrayFromImage(nrrd_image) with torch.no_grad(): start = 0 end = start+batch_size while end <= input_image_array.shape[0]: pred = model(torch.from_numpy(np.expand_dims( input_image_array[start:end], axis=1).astype(np.float32)).to(device)) if first: result = pred.detach().data.cpu() first = False else: result = torch.cat((result, pred.detach().data.cpu())) print(result.shape) start = end end = start + batch_size result = torch.argmax(result, dim=1, keepdim=True) array_to_write = result.data.numpy().squeeze() array_to_write = crop_image_to_orig_size( array_to_write, SimpleITK.GetArrayFromImage(nrrd_image).shape) print(array_to_write.shape) image_to_write = SimpleITK.GetImageFromArray(array_to_write) image_to_write.SetSpacing(nrrd_image.GetSpacing()) image_to_write.SetOrigin(nrrd_image.GetOrigin()) image_to_write.SetDirection(nrrd_image.GetDirection()) SimpleITK.WriteImage(SimpleITK.Cast(image_to_write, SimpleITK.sitkUInt8), os.path.join(dir_path, 'temp_CT_output.nrrd')) print('Ended python script...') diff --git a/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.cpp b/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.cpp index 84db3968ec..5aad043f1e 100644 --- a/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.cpp +++ b/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.cpp @@ -1,178 +1,183 @@ /*=================================================================== 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 // Qmitk #include "BoneSegmentation.h" // Qt #include // mitk image #include - +// #include #include #include const std::string BoneSegmentation::VIEW_ID = "org.mitk.views.bonesegmentation"; void BoneSegmentation::SetFocus() { m_Controls.buttonPerformImageProcessing->setFocus(); } void BoneSegmentation::CreateQtPartControl(QWidget *parent) { - m_process = new QProcess(); - // create GUI widgets from the Qt Designer's .ui file + m_BoneSegmentationPythonProcess = new QProcess(); m_Controls.setupUi(parent); m_Controls.buttonPerformImageProcessing->setText("Start Bone Segmentation"); m_Controls.buttonPerformImageProcessing->setEnabled(false); connect(m_Controls.buttonPerformImageProcessing, &QPushButton::clicked, this, &BoneSegmentation::DoImageProcessing); - connect(m_process, SIGNAL(finished(int, QProcess::ExitStatus )),this, SLOT(doSomethingWhenItsDone())); + connect(m_BoneSegmentationPythonProcess, SIGNAL(finished(int, QProcess::ExitStatus )),this, SLOT(DoBoneSegmentationProcessFinished())); connect(&m_Thread, SIGNAL(started()), this, SLOT(DoSaveTempNrrd())); connect(&m_Thread, SIGNAL(finished()), this, SLOT(DoStartBoneSegmentationProcess())); // connect(this, SIGNAL(FinishedSaveTempNrrd()), this, SLOT(DoStartBoneSegmentationProcess())); + + // auto isImagePredicate = mitk::GetImageStatisticsImagePredicate(); + m_Controls.m_ImageSelector->SetDataStorage(GetDataStorage()); + // m_Controls.imageSelector->SetPredicate(isImagePredicate); + + connect(this->m_Controls.m_ImageSelector, + static_cast(&QComboBox::currentIndexChanged), + this, + &BoneSegmentation::OnImageSelectorChanged); } -void BoneSegmentation::OnSelectionChanged(berry::IWorkbenchPart::Pointer /*source*/, - const QList &nodes) +void BoneSegmentation::OnImageSelectorChanged() { - // iterate all selected objects, adjust warning visibility - foreach (mitk::DataNode::Pointer node, nodes) + auto selectedImageNode = m_Controls.m_ImageSelector->GetSelectedNode(); + if (selectedImageNode != m_selectedImageNode) { - if (node.IsNotNull() && dynamic_cast(node->GetData())) + m_selectedImageNode = selectedImageNode; + if (m_selectedImageNode.IsNotNull()) { - m_Controls.labelWarning->setVisible(false); - m_Controls.buttonPerformImageProcessing->setEnabled(true); - return; + m_Controls.labelWarning->setVisible(false); + m_Controls.buttonPerformImageProcessing->setEnabled(true); + return; } + m_Controls.labelWarning->setText("Please select an image!"); + m_Controls.labelWarning->setVisible(true); + m_Controls.buttonPerformImageProcessing->setEnabled(false); } - - m_Controls.labelWarning->setText("Please select an image!"); - m_Controls.labelWarning->setVisible(true); - m_Controls.buttonPerformImageProcessing->setEnabled(false); } void BoneSegmentation::DoSaveTempNrrd() { - QList nodes = this->GetDataManagerSelection(); - if (nodes.empty()) - return; - - mitk::DataNode *node = nodes.front(); - - if (!node) + if (!m_selectedImageNode) { // Nothing selected. Inform the user and return QMessageBox::information(nullptr, "Template", "Please load and select an image before starting image processing."); return; } // a node itself is not very useful, we need its data item (the image) - mitk::BaseData *data = node->GetData(); + mitk::BaseData *data = m_selectedImageNode->GetData(); if (data) { // test if this data item is an image or not (could also be a surface or something totally different) mitk::Image *mitk_image = dynamic_cast(data); if (mitk_image) { std::stringstream message; std::string name; message << "Performing image processing for image "; - if (node->GetName(name)) + if (m_selectedImageNode->GetName(name)) { // a property called "name" was found for this DataNode message << "'" << name << "'"; } message << "."; MITK_INFO << message.str(); MITK_INFO << "[START] StartPythonProcess()"; std::string file_name = mitk::IOUtil::GetTempPath() + "temp_CT_1.nrrd"; mitk::IOUtil::Save(mitk_image, file_name); } } m_Thread.quit(); } void BoneSegmentation::DoImageProcessing() { m_Controls.labelWarning->setVisible(true); m_Controls.labelWarning->setText("This might take a while.\nDepending ob your machine up to 20 minutes or more."); m_Controls.buttonPerformImageProcessing->setEnabled(false); + m_Controls.m_ImageSelector->setEnabled(false); m_Thread.start(); } void BoneSegmentation::DoStartBoneSegmentationProcess() { + MITK_INFO << "[Start] DoStartBoneSegmentationProcess()"; QString fileName(":/segment.py"); QString pythonFileName = QString::fromStdString(mitk::IOUtil::GetTempPath() + "segment.py"); - MITK_INFO << "[Start] DoStartBoneSegmentationProcess()"; QFile::copy(fileName, pythonFileName); - QString pretrainedNetResources(":/trained_bone_seg_unet.pth.tar"); - QString pretrainedNet = QString::fromStdString(mitk::IOUtil::GetTempPath() + "trained_bone_seg_unet.pth.tar"); - QFile::copy(pretrainedNetResources, pretrainedNet); + QString pretrainedNetResourcesPath(":/trained_bone_seg_unet.pth.tar"); + //QString pretrainedNetResourcesPath("/media/kleina/Data/ubuntu_dev/201904mitk/src/Plugins/org.mitk.gui.qt.bonesegmentation/resources/trained_bone_seg_unet.pth.tar"); + QString pretrainedNetTmpPath = QString::fromStdString(mitk::IOUtil::GetTempPath() + "trained_bone_seg_unet.pth.tar"); + bool test = QFile::copy(pretrainedNetResourcesPath, pretrainedNetTmpPath); + if(test) + MITK_WARN << "Bla"; QString programCall("python"); QStringList arguments = QStringList() << pythonFileName; - //QStringList arguments = QStringList() << "/media/kleina/Data/ubuntu_dev/201904mitk/src/Plugins/org.mitk.gui.qt.bonesegmentation/resources/segment.py"; MITK_INFO << programCall; + m_BoneSegmentationPythonProcess->start(programCall, arguments); - m_process->start(programCall, arguments); MITK_INFO << "[END] DoStartBoneSegmentationProcess()"; } -void BoneSegmentation::doSomethingWhenItsDone() +void BoneSegmentation::DoBoneSegmentationProcessFinished() { MITK_INFO << "Ended calculation thread."; m_Controls.buttonPerformImageProcessing->setText("Start Bone Segmentation"); m_Controls.buttonPerformImageProcessing->setEnabled(true); try { mitk::IOUtil::Load(mitk::IOUtil::GetTempPath() + "temp_CT_output.nrrd", *GetDataStorage()); - GetDataStorage().GetPointer()->GetNamedNode("temp_CT_output")->SetName("Bone_segmentation"); + GetDataStorage().GetPointer()->GetNamedNode("temp_CT_output")->SetName("Bone_seg_" + m_selectedImageNode->GetName()); } catch(...) { - MITK_WARN << "Error loading file. Maybe it does not exist." ; + MITK_WARN << "Error loading output file. Maybe it does not exist." ; } std::string temp_CT = mitk::IOUtil::GetTempPath() + "temp_CT_1.nrrd"; if(remove(temp_CT.c_str()) != 0) MITK_WARN << "Error deleting file" ; else MITK_INFO << "File successfully deleted" ; std::string temp_output = mitk::IOUtil::GetTempPath() + "temp_CT_output.nrrd"; if(remove(temp_output.c_str()) != 0) MITK_WARN << "Error deleting file" ; else MITK_INFO << "File successfully deleted" ; m_Controls.labelWarning->setVisible(false); + m_Controls.m_ImageSelector->setEnabled(true); } diff --git a/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.h b/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.h index 675cd98f27..3b3155aef1 100644 --- a/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.h +++ b/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentation.h @@ -1,70 +1,70 @@ /*=================================================================== 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 BoneSegmentation_h #define BoneSegmentation_h #include #include #include "ui_BoneSegmentationControls.h" #include #include /** \brief BoneSegmentation - \warning This class is not yet documented. Use "git blame" and ask the author to provide basic documentation. + \warning The BoneSegmentation plugin provides an automatic bone segmentation for CT images based on deep learning. + At this moment, only CT images can be segmented. The segmentation is done on 2D axial slices. The network was trained + using data from the multiple myeloma project. It was trained on a very limited amount of training data and needs to + be tested in "real world scenarios". \sa QmitkAbstractView \ingroup ${plugin_target}_internal */ class BoneSegmentation : public QmitkAbstractView { // this is needed for all Qt objects that should have a Qt meta-object // (everything that derives from QObject and wants to have signal/slots) Q_OBJECT public: static const std::string VIEW_ID; signals: void FinishedSaveTempNrrd(); protected slots: /// \brief Called when the user clicks the GUI button + void OnImageSelectorChanged(); void DoSaveTempNrrd(); void DoImageProcessing(); void DoStartBoneSegmentationProcess(); - void doSomethingWhenItsDone(); + void DoBoneSegmentationProcessFinished(); protected: virtual void CreateQtPartControl(QWidget *parent) override; - virtual void SetFocus() override; - /// \brief called by QmitkFunctionality when DataManager's selection has changed - virtual void OnSelectionChanged(berry::IWorkbenchPart::Pointer source, - const QList &nodes) override; - Ui::BoneSegmentationControls m_Controls; - QProcess* m_process; + mitk::DataNode::ConstPointer m_selectedImageNode = nullptr; + QProcess* m_BoneSegmentationPythonProcess; QThread m_Thread; }; #endif // BoneSegmentation_h diff --git a/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentationControls.ui b/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentationControls.ui index b601753fba..f4d08c572a 100644 --- a/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentationControls.ui +++ b/Plugins/org.mitk.gui.qt.bonesegmentation/src/internal/BoneSegmentationControls.ui @@ -1,64 +1,74 @@ BoneSegmentationControls 0 0 222 161 0 0 QmitkTemplate + + + QLabel { color: rgb(255, 0, 0) } Please select an image! Do image processing Do Something Qt::Vertical QSizePolicy::Expanding 20 220 + + + QmitkDataStorageComboBoxWithSelectNone + QComboBox +
QmitkDataStorageComboBoxWithSelectNone.h
+ +