diff --git a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp
index 4ce3dfabd5..3413ae4e52 100644
--- a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp
+++ b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp
@@ -1,977 +1,1057 @@
 /*===================================================================
 
 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 <berryISelectionService.h>
 #include <berryIWorkbenchWindow.h>
 
 // Qt
 #include <QColorDialog>
 #include <QMessageBox>
 #include <QFuture>
 #include <QtConcurrent>
 
 // Qmitk
 #include "QmitkActiveLearning.h"
 
 // MITK
 #include <usModuleRegistry.h>
 #include <mitkImage.h>
 #include <mitkImageWriteAccessor.h>
 #include <mitkImagePixelReadAccessor.h>
 #include <mitkLabel.h>
 #include <mitkBaseGeometry.h>
 #include <mitkNumericTypes.h>
 #include <mitkPixelType.h>
 #include <mitkImageCast.h>
 #include <mitkImageAccessByItk.h>
 #include <mitkLookupTable.h>
 #include <mitkLookupTableProperty.h>
 #include <mitkRenderingModeProperty.h>
 
 // ITK
 #include <itkDiscreteGaussianImageFilter.h>
 #include <itkGradientMagnitudeRecursiveGaussianImageFilter.h>
 #include <itkLaplacianRecursiveGaussianImageFilter.h>
 #include <itkHessianMatrixEigenvalueImageFilter.h>
 #include <itkStructureTensorEigenvalueImageFilter.h>
 #include <itkHessianRecursiveGaussianImageFilter.h>
 #include <itkSymmetricEigenAnalysisImageFilter.h>
 #include <itkImageRegionIterator.h>
 
 typedef ActiveLearning::FeaturePixelType FeaturePixelType;
 typedef ActiveLearning::AnnotationPixelType AnnotationPixelType;
 typedef ActiveLearning::LabelPixelType LabelPixelType;
 typedef ActiveLearning::FeatureMatrixType FeatureMatrixType;
 typedef ActiveLearning::LabelVectorType LabelVectorType;
 
 // Returns true if list has at least one entry and all entries are valid mitk::Images, otherwise false
 static bool SelectionAllImages(const QList<mitk::DataNode::Pointer>& nodes)
 {
     if (nodes.empty())
     {
         return false;
     }
     for (const auto& node : nodes)
     {
         if(!(node.IsNotNull() && dynamic_cast<mitk::Image*>(node->GetData()) != nullptr)) return false;
     }
     return true;
 }
 
 // QColor to mitk::Color
 static mitk::Color QColorToMitkColor(const QColor& qcolor)
 {
     mitk::Color color;
     color.SetRed((float)qcolor.red() / 255);
     color.SetGreen((float)qcolor.green() / 255);
     color.SetBlue((float)qcolor.blue() / 255);
     return color;
 }
 
 // For debugging
 static void PrintAllLabels(mitk::LabelSetImage* image)
 {
     for (auto it=image->GetActiveLabelSet()->IteratorBegin(); it!=image->GetActiveLabelSet()->IteratorConstEnd(); ++it)
     {
         MITK_INFO << "Key: " << it->first << " - Name: " << it->second->GetName() << " - Value: " << it->second->GetValue() << " - Color: " << it->second->GetColor();
     }
 }
 
 // Make values of labels a consistent range
 static void FillLabelValues(mitk::LabelSetImage* image)
 {
     int value(0);
     for (auto it=image->GetActiveLabelSet()->IteratorBegin(); it!=image->GetActiveLabelSet()->IteratorConstEnd(); ++it)
     {
         it->second->SetValue(value);
         value++;
     }
     image->GetActiveLabelSet()->SetActiveLabel(0);
 }
 
 // Fill image with zeros
 static void FillWithZeros(mitk::Image* image)
 {
     unsigned int size = image->GetPixelType().GetSize();
     for (unsigned int i=0; i<image->GetDimension(); i++)
     {
         size *= image->GetDimension(i);
     }
     for (unsigned int t=0; t<image->GetTimeSteps(); t++)
     {
         mitk::ImageWriteAccessor accessor(image, image->GetVolumeData(0));
         memset(accessor.GetData(), 0, size);
     }
 }
 
 template<typename MatrixElementType>
 static Eigen::Matrix<MatrixElementType, Eigen::Dynamic, Eigen::Dynamic> Transform(const std::vector<mitk::Image::Pointer> images)
 {
     // Find size for output matrix [number of voxels, number of feature images]
     unsigned int size = images[0]->GetDimension(0);
     for (unsigned int i=1; i<3; ++i)
     {
         size *= images[0]->GetDimension(i);
     }
 
     Eigen::Matrix<MatrixElementType, Eigen::Dynamic, Eigen::Dynamic> outputMatrix(size, images.size());
 
     for (unsigned int i=0; i<images.size(); ++i)
     {
         typename itk::Image<MatrixElementType, 3>::Pointer imageItk;
         mitk::CastToItkImage(images[i], imageItk);
         outputMatrix.col(i) = Eigen::Matrix<MatrixElementType, Eigen::Dynamic, 1>::Map(imageItk->GetBufferPointer(), size);
     }
 
     return outputMatrix;
 }
 
 template<typename MatrixElementType, typename OutputPixelType>
-static mitk::Image::Pointer Transform(const Eigen::Matrix<MatrixElementType, Eigen::Dynamic, 1> &inputMatrix, const mitk::Image::Pointer referenceImage)
+static mitk::Image::Pointer Transform(const Eigen::Matrix<MatrixElementType, Eigen::Dynamic, 1> &inputMatrix,
+                                      const mitk::Image::Pointer referenceImage)
 {
     typename itk::Image<OutputPixelType, 3>::Pointer imageItk;
     auto outputImage = mitk::Image::New();
     outputImage->Initialize(mitk::MakeScalarPixelType<OutputPixelType>(), *(referenceImage->GetTimeGeometry()->Clone()));
     mitk::CastToItkImage(outputImage, imageItk);
 
     auto it = itk::ImageRegionIterator<itk::Image<OutputPixelType, 3>>(imageItk, imageItk->GetLargestPossibleRegion());
     int i = 0;
     while (!it.IsAtEnd())
     {
         it.Set(inputMatrix(i, 0));
         ++it;
         ++i;
     }
 
     outputImage = mitk::GrabItkImageMemory(imageItk);
     return outputImage;
 }
 
+template<typename MatrixElementType, typename OutputPixelType>
+static mitk::Image::Pointer Transform(const Eigen::Matrix<MatrixElementType, Eigen::Dynamic, Eigen::Dynamic> &inputMatrix,
+                                      const mitk::Image::Pointer referenceImage)
+{
+    // Create ITK image
+    typedef itk::VectorImage<OutputPixelType, 3> VectorImageType;
+    auto imageItk = VectorImageType::New();
+    typename VectorImageType::IndexType start;
+    start.Fill(0);
+    typename VectorImageType::SizeType size;
+    for (int i=0; i<3; ++i)
+    {
+        size[i] = referenceImage->GetDimension(i);
+    }
+    typename VectorImageType::RegionType region(start, size);
+    imageItk->SetRegions(region);
+    imageItk->SetVectorLength(inputMatrix.cols());
+    imageItk->Allocate();
+
+    auto outputImage = mitk::Image::New();
+
+    auto it = itk::ImageRegionIterator<itk::VectorImage<OutputPixelType, 3>>(imageItk, imageItk->GetLargestPossibleRegion());
+    int i = 0;
+    while (!it.IsAtEnd())
+    {
+        itk::VariableLengthVector<OutputPixelType> pixel;
+        pixel.SetSize(inputMatrix.cols());
+        for (int j=0; j<inputMatrix.cols(); ++j)
+        {
+            pixel[j] = static_cast<OutputPixelType>(inputMatrix(i, j));
+        }
+        it.Set(pixel);
+        ++it;
+        ++i;
+    }
+
+    outputImage = mitk::ImportItkImage(imageItk);
+    outputImage->SetClonedTimeGeometry(referenceImage->GetTimeGeometry());
+    return outputImage;
+}
+
 template<typename T>
-static void PrintMatrix(const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> dataMatrix, int maxRows = 0)
+static void PrintMatrix(const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> dataMatrix,
+                        int maxRows = 0)
 {
     if (maxRows == 0 || maxRows > dataMatrix.rows()) maxRows = dataMatrix.rows();
 
     MITK_INFO << "---------------------";
     for (int i=0; i<maxRows; ++i)
     {
         std::stringstream ss;
         for (int j=0; j<dataMatrix.cols(); ++j)
         {
             ss << std::fixed << std::setprecision(2) << dataMatrix(i, j) << " ";
         }
         MITK_INFO << ss.str();
     }
     MITK_INFO << "---------------------";
 }
 
 template<typename T1, typename T2 = T1>
-static void PrintMatrix(const Eigen::Matrix<T1, Eigen::Dynamic, Eigen::Dynamic> dataMatrix, const Eigen::Matrix<T2, Eigen::Dynamic, Eigen::Dynamic> labelMatrix, int maxRows = 0)
+static void PrintMatrix(const Eigen::Matrix<T1, Eigen::Dynamic, Eigen::Dynamic> dataMatrix,
+                        const Eigen::Matrix<T2, Eigen::Dynamic, Eigen::Dynamic> labelMatrix,
+                        int maxRows = 0)
 {
     if (labelMatrix.rows() < dataMatrix.rows()) return;
 
     if (maxRows == 0 || maxRows > dataMatrix.rows()) maxRows = dataMatrix.rows();
 
     MITK_INFO << "---------------------";
     for (int i=0; i<maxRows; ++i)
     {
         std::stringstream ss;
         for (int j=0; j<labelMatrix.cols(); ++j)
         {
             ss << labelMatrix(i, j) << " ";
         }
         ss << "- ";
         for (int j=0; j<dataMatrix.cols(); ++j)
         {
             ss << std::fixed << std::setprecision(2) << dataMatrix(i, j) << " ";
         }
         MITK_INFO << ss.str();
     }
     MITK_INFO << "---------------------";
 }
 
 /* ==================================================================
  * FEATURES
  * =============================================================== */
 
 template<typename PixelType, unsigned int imageDimension>
-static void GaussianSmoothing(const itk::Image<PixelType, imageDimension>* inputImage, const double sigma, mitk::Image::Pointer outputImage)
+static void GaussianSmoothing(const itk::Image<PixelType, imageDimension>* inputImage,
+                              const double sigma, mitk::Image::Pointer outputImage)
 {
     typedef itk::Image<PixelType, imageDimension> ImageType;
     typedef itk::Image<FeaturePixelType, imageDimension> FeatureImageType;
     auto filter = itk::DiscreteGaussianImageFilter<ImageType, FeatureImageType>::New();
     filter->SetInput(inputImage);
     filter->SetVariance(sigma*sigma);
     filter->Update();
     mitk::GrabItkImageMemory(filter->GetOutput(), outputImage);
 }
 
 template<typename PixelType, unsigned int imageDimension>
-static void GaussianGradientMagnitude(const itk::Image<PixelType, imageDimension>* inputImage, const double sigma, mitk::Image::Pointer outputImage)
+static void GaussianGradientMagnitude(const itk::Image<PixelType, imageDimension>* inputImage,
+                                      const double sigma, mitk::Image::Pointer outputImage)
 {
     typedef itk::Image<PixelType, imageDimension> ImageType;
     typedef itk::Image<FeaturePixelType, imageDimension> FeatureImageType;
     auto filter = itk::GradientMagnitudeRecursiveGaussianImageFilter<ImageType, FeatureImageType>::New();
     filter->SetInput(inputImage);
     filter->SetSigma(sigma);
     filter->Update();
     mitk::GrabItkImageMemory(filter->GetOutput(), outputImage);
 }
 
 template<typename PixelType, unsigned int imageDimension>
-static void LaplacianOfGaussian(const itk::Image<PixelType, imageDimension>* inputImage, const double sigma, mitk::Image::Pointer outputImage)
+static void LaplacianOfGaussian(const itk::Image<PixelType, imageDimension>* inputImage,
+                                const double sigma, mitk::Image::Pointer outputImage)
 {
     typedef itk::Image<PixelType, imageDimension> ImageType;
     typedef itk::Image<FeaturePixelType, imageDimension> FeatureImageType;
     auto filter = itk::LaplacianRecursiveGaussianImageFilter<ImageType, FeatureImageType>::New();
     filter->SetInput(inputImage);
     filter->SetSigma(sigma);
     filter->Update();
     mitk::GrabItkImageMemory(filter->GetOutput(), outputImage);
 }
 
 //template<typename PixelType, unsigned int imageDimension>
-//static void StructureTensorEigenvalues(const itk::Image<PixelType, imageDimension>* inputImage, float sigma, std::vector<mitk::Image::Pointer> outputImages)
+//static void StructureTensorEigenvalues(const itk::Image<PixelType, imageDimension>* inputImage,
+//                                       float sigma, std::vector<mitk::Image::Pointer> outputImages)
 //{
 //    typedef itk::Image<PixelType, imageDimension> ImageType;
 //    auto filter = itk::StructureTensorEigenvalueImageFilter<ImageType>::New();
 //    filter->SetInput(inputImage);
 //    filter->SetInnerScale(sigma);
 //    filter->SetOuterScale(sigma);
 //    filter->Update();
 //    for (unsigned int i=0; i<filter->GetNumberOfOutputs(); i++)
 //    {
 //        mitk::GrabItkImageMemory(filter->GetOutput(i), outputImages[i]);
 //    }
 //}
 
 //template<typename PixelType, unsigned int imageDimension>
-//static void HessianEigenvalues(const itk::Image<PixelType, imageDimension>* inputImage, float sigma, std::vector<mitk::Image::Pointer> outputImages)
+//static void HessianEigenvalues(const itk::Image<PixelType, imageDimension>* inputImage,
+//                               float sigma, std::vector<mitk::Image::Pointer> outputImages)
 //{
 //    typedef itk::Image<PixelType, imageDimension> ImageType;
 //    typedef itk::Image<itk::SymmetricSecondRankTensor<PixelType, imageDimension>, imageDimension> TensorImageType;
 //    auto filter = itk::HessianRecursiveGaussianImageFilter<ImageType, TensorImageType>::New();
 
 //    ImageType::Pointer o1, o2, o3;
 //    o1->Allocate();
 //    o2->Allocate();
 //    o3->Allocate();
 
 //    filter->SetInput(inputImage);
 //    filter->SetSigma(sigma);
 //    filter->Update();
 //    TensorImageType::Pointer tensorImage = filter->GetOutput();
 
 //    itk::ImageRegionIterator<TensorImageType> tensorIt(tensorImage, tensorImage->GetLargestPossibleRegion());
 //    itk::ImageRegionIterator<ImageType> o1It(o1, o1->GetLargestPossibleRegion());
 //    itk::ImageRegionIterator<ImageType> o2It(o2, o2->GetLargestPossibleRegion());
 //    itk::ImageRegionIterator<ImageType> o3It(o3, o3->GetLargestPossibleRegion());
 
 //    while (!tensorIt.IsAtEnd())
 //    {
 //	itk::SymmetricSecondRankTensor<PixelType, imageDimension>::EigenValue
 
 //    for (unsigned int i=0; i<filter->GetNumberOfOutputs(); i++)
 //    {
 //        mitk::GrabItkImageMemory(filter->GetOutput(i), outputImages[i]);
 //    }
 //}
 
 /* ==================================================================
  * PUBLIC SLOTS
  * =============================================================== */
 
 
 void ActiveLearning::Initialize()
 {
     // Get selected nodes and check again if these are all images
     m_Nodes = this->GetDataManagerSelection();
     if (!SelectionAllImages(m_Nodes)) return;
 
     m_Controls.m_InitializePushButton->setDisabled(true);
 
     // Set names to the label (again)
     QString nameList = QString::fromStdString(m_Nodes[0]->GetName());
     if (m_Nodes.length() >= 2)
     {
         for (int i=1; i<m_Nodes.length(); ++i)
         {
             nameList += QString("<br />");
             nameList += QString::fromStdString(m_Nodes[i]->GetName());
         }
     }
     m_Controls.m_InitializeLabel->setText(nameList);
 
     // =======================================
     // PREDICTION IMAGE
     // =======================================
 
     m_PredictionImage = mitk::Image::New();
 
     try
     {
         mitk::Image::Pointer referenceImage = dynamic_cast<mitk::Image*>(m_Nodes[0]->GetData());
         m_PredictionImage->Initialize(referenceImage);
     }
     catch (mitk::Exception& e)
     {
         MITK_ERROR << "Exception caught: " << e.GetDescription();
         QMessageBox::information(m_Parent, "Error", "Could not initialize prediction image");
         return;
     }
 
     FillWithZeros(m_PredictionImage);
 
     m_PredictionNode = mitk::DataNode::New();
     m_PredictionNode->SetData(m_PredictionImage);
     m_PredictionNode->SetName("Predictions");
     m_PredictionNode->SetColor(1., 1. ,1.);
     m_PredictionNode->SetBoolProperty("binary", false);
     m_PredictionNode->SetProperty("opacity", mitk::FloatProperty::New(0.0f));
 //    m_PredictionNode->SetBoolProperty("helper object", true);
     this->GetDataStorage()->Add(m_PredictionNode, m_Nodes[0]);
 
     // =======================================
     // SEGMENTATION IMAGE
     // =======================================
 
     m_SegmentationImage = mitk::Image::New();
 
     try
     {
         mitk::Image::Pointer referenceImage = dynamic_cast<mitk::Image*>(m_Nodes[0]->GetData());
         m_SegmentationImage->Initialize(mitk::MakeScalarPixelType<AnnotationPixelType>(), *(referenceImage->GetTimeGeometry()->Clone()));
     }
     catch (mitk::Exception& e)
     {
         MITK_ERROR << "Exception caught: " << e.GetDescription();
         QMessageBox::information(m_Parent, "Error", "Could not initialize segmentation image");
         return;
     }
 
     FillWithZeros(m_SegmentationImage);
 
     m_SegmentationNode = mitk::DataNode::New();
     m_SegmentationNode->SetData(m_SegmentationImage);
     m_SegmentationNode->SetName("Segmentation");
     m_SegmentationNode->SetColor(1., 1., 1.);
     m_SegmentationNode->SetBoolProperty("binary", false);
 //    m_PredictionNode->SetBoolProperty("helper object", true);
     this->GetDataStorage()->Add(m_SegmentationNode, m_Nodes[0]);
 
     // =======================================
     // ANNOTATION IMAGE
     // =======================================
 
     m_AnnotationImage = mitk::Image::New();
 
     try
     {
         mitk::Image::Pointer referenceImage = dynamic_cast<mitk::Image*>(m_Nodes[0]->GetData());
         m_AnnotationImage->Initialize(mitk::MakeScalarPixelType<AnnotationPixelType>(), *(referenceImage->GetTimeGeometry()->Clone()));
     }
     catch (mitk::Exception& e)
     {
         MITK_ERROR << "Exception caught: " << e.GetDescription();
         QMessageBox::information(m_Parent, "Error", "Could not initialize annotation image");
         return;
     }
 
     FillWithZeros(m_AnnotationImage);
 
     m_AnnotationNode = mitk::DataNode::New();
     m_AnnotationNode->SetData(m_AnnotationImage);
     m_AnnotationNode->SetName("Labels");
     m_AnnotationNode->SetColor(1., 1., 1.);
     m_AnnotationNode->SetBoolProperty("binary", false);
     m_AnnotationNode->SetProperty("opacity", mitk::FloatProperty::New(1.0f));
 //    m_AnnotationNode->SetBoolProperty("helper object", true);
     this->GetDataStorage()->Add(m_AnnotationNode, m_Nodes[0]);
 
     // Convert input images to FeaturePixelType
     for (auto node : m_Nodes)
     {
         mitk::Image::Pointer image = dynamic_cast<mitk::Image*>(node->GetData());
         auto itkImage = itk::Image<FeaturePixelType, 3>::New();
         mitk::CastToItkImage(image, itkImage);
         image = mitk::GrabItkImageMemory(itkImage);
         node->SetData(image);
     }
 
     // Calculate features
     for (const auto node : m_Nodes)
     {
         mitk::Image::Pointer currentImage = dynamic_cast<mitk::Image*>(node->GetData());
         QFuture<std::vector<std::pair<mitk::Image::Pointer, std::string>>> future;
         future = QtConcurrent::run(this, &ActiveLearning::CalculateFeatures, currentImage);
         auto futureWatcher = new QFutureWatcher<std::vector<std::pair<mitk::Image::Pointer, std::string>>>();
         futureWatcher->setFuture(future);
         connect(futureWatcher, SIGNAL(finished()), this, SLOT(OnInitializationFinished()));
         m_FeatureCalculationWatchers.push_back(futureWatcher);
     }
 
     // Interactor
     auto activeLearningLib = us::ModuleRegistry::GetModule("MitkCLActiveLearning");
     m_Interactor = mitk::ActiveLearningInteractor::New();
     m_Interactor->LoadStateMachine("Paint.xml", activeLearningLib);
     m_Interactor->SetEventConfig("PaintConfig.xml", activeLearningLib);
     m_Interactor->SetDataNode(m_AnnotationNode);
 
     // Automatically add first label
     OnAddLabelPushButtonClicked();
 
     m_Active = true;
 }
 
 
 /* ==================================================================
  * PUBLIC
  * =============================================================== */
 
 
 ActiveLearning::ActiveLearning() :
     m_Parent(nullptr),
     m_AnnotationImage(nullptr),
     m_AnnotationNode(nullptr),
     m_PredictionImage(nullptr),
     m_PredictionNode(nullptr),
     m_SegmentationImage(nullptr),
     m_SegmentationNode(nullptr),
     m_Active(false),
     m_NumberOfTrees(50),
     m_MaximumTreeDepth(10),
     m_SamplesPerTree(0.66),
     m_PredictionMatrix(nullptr)
 {
 
 }
 
 ActiveLearning::~ActiveLearning()
 {
 
 }
 
 void ActiveLearning::CreateQtPartControl( QWidget *parent )
 {
     m_Controls.setupUi(parent);
     m_Parent = parent;
 
     // Label model
     m_LabelListModel = new QStandardItemModel(0, 3, this);
     m_Controls.m_LabelTableView->setModel(m_LabelListModel);
     m_Controls.m_LabelTableView->horizontalHeader()->setDefaultSectionSize(20);
     m_Controls.m_LabelTableView->verticalHeader()->setDefaultSectionSize(20);
     NotEditableDelegate* itemDelegate = new NotEditableDelegate(parent);
     m_Controls.m_LabelTableView->setItemDelegateForColumn(1, itemDelegate);
     connect(m_Controls.m_LabelTableView, SIGNAL(doubleClicked(QModelIndex)),
             this, SLOT(OnColorIconDoubleClicked(QModelIndex)));
     connect(m_Controls.m_LabelTableView->selectionModel(), SIGNAL(selectionChanged(QItemSelection, QItemSelection)),
             this, SLOT(OnLabelListSelectionChanged(QItemSelection, QItemSelection)));
     connect(m_LabelListModel, SIGNAL(dataChanged(QModelIndex, QModelIndex)),
             this, SLOT(OnLabelNameChanged(QModelIndex, QModelIndex)));
 
     // Buttons
     connect(m_Controls.m_InitializePushButton, SIGNAL(clicked()),
             this, SLOT(Initialize()));
     connect(m_Controls.m_AddLabelPushButton, SIGNAL(clicked()),
             this, SLOT(OnAddLabelPushButtonClicked()));
     connect(m_Controls.m_RemoveLabelPushButton, SIGNAL(clicked()),
             this, SLOT(OnRemoveLabelPushButtonClicked()));
     connect(m_Controls.m_PaintToolButton, SIGNAL(clicked()),
             this, SLOT(OnPaintToolButtonClicked()));
     connect(m_Controls.m_EraseToolButton, SIGNAL(clicked()),
             this, SLOT(OnEraseToolButtonClicked()));
     connect(m_Controls.m_UpdatePredictionsPushButton, SIGNAL(clicked()),
             this, SLOT(OnUpdatePredictionsPushButtonClicked()));
 
-    connect(m_PredictionCalculationWatcher, SIGNAL(finished()),
-            this, SLOT(OnPredictionCalculationFinished()));
-
     // Set start configuration
     m_Controls.m_LabelControlsFrame->setVisible(false);
     SetInitializeReady(false);
 }
 
 void ActiveLearning::ResetLabels()
 {
     for (int i=0; i<m_LabelListModel->rowCount(); i++)
     {
         m_LabelListModel->item(i, 1)->setText(QString::number(i + 1));
     }
 }
 
 std::vector<std::pair<mitk::Image::Pointer, std::string> > ActiveLearning::CalculateFeatures(const mitk::Image::Pointer inputImage)
 {
     std::vector<std::pair<mitk::Image::Pointer, std::string>> result;
 
     // TODO: Get features from preference page
     std::vector<float> sigmas = {0.7, 1.6};
 
     for (auto sigma : sigmas)
     {
         std::stringstream ss;
 
         auto gaussImage = mitk::Image::New();
         AccessByItk_n(inputImage, GaussianSmoothing, (sigma, gaussImage));
         ss << "GaussianSmoothing (" << std::fixed << std::setprecision(2) << sigma << ")";
         result.push_back(std::pair<mitk::Image::Pointer, std::string>(gaussImage, ss.str()));
         ss.str("");
 
         auto gradMagImage = mitk::Image::New();
         AccessByItk_n(inputImage, GaussianGradientMagnitude, (sigma, gradMagImage));
         ss << "GaussianGradientMagnitude (" << std::fixed << std::setprecision(2) << sigma << ")";
         result.push_back(std::pair<mitk::Image::Pointer, std::string>(gradMagImage, ss.str()));
         ss.str("");
 
         auto logImage = mitk::Image::New();
         AccessByItk_n(inputImage, LaplacianOfGaussian, (sigma, logImage));
         ss << "LaplacianOfGaussian (" << std::fixed << std::setprecision(2) << sigma << ")";
         result.push_back(std::pair<mitk::Image::Pointer, std::string>(logImage, ss.str()));
         ss.str("");
 
 //        auto structImage1 = mitk::Image::New();
 //        auto structImage2 = mitk::Image::New();
 //        auto structImage3 = mitk::Image::New();
 //        std::vector<mitk::Image::Pointer> structImages = {structImage1, structImage2, structImage3};
 //        AccessByItk_n(inputImage, StructureTensorEigenvalues, (sigma, structImages));
 //        ss << "StructureTensorEigenvalue1 (" << std::fixed << std::setprecision(2) << sigma << ")";
 //        result.push_back(std::pair<mitk::Image::Pointer, std::string>(structImage1, ss.str()));
 //        ss.str("");
 //        ss << "StructureTensorEigenvalue2 (" << std::fixed << std::setprecision(2) << sigma << ")";
 //        result.push_back(std::pair<mitk::Image::Pointer, std::string>(structImage2, ss.str()));
 //        ss.str("");
 //        if (inputImage->GetDimension() == 3)
 //        {
 //            ss << "StructureTensorEigenvalue3 (" << std::fixed << std::setprecision(2) << sigma << ")";
 //            result.push_back(std::pair<mitk::Image::Pointer, std::string>(structImage3, ss.str()));
 //            ss.str("");
 //        }
 
 //        auto hessianImage1 = mitk::Image::New();
 //        auto hessianImage2 = mitk::Image::New();
 //        auto hessianImage3 = mitk::Image::New();
 //        std::vector<mitk::Image::Pointer> hessianImages = {hessianImage1, hessianImage2, hessianImage3};
 //        AccessByItk_n(inputImage, HessianEigenvalues, (sigma, hessianImages));
 //        ss << "HessianEigenvalue1 (" << std::fixed << std::setprecision(2) << sigma << ")";
 //        result.push_back(std::pair<mitk::Image::Pointer, std::string>(hessianImage1, ss.str()));
 //        ss.str("");
 //        ss << "HessianEigenvalue2 (" << std::fixed << std::setprecision(2) << sigma << ")";
 //        result.push_back(std::pair<mitk::Image::Pointer, std::string>(hessianImage2, ss.str()));
 //        ss.str("");
 //        if (inputImage->GetDimension() == 3)
 //        {
 //            ss << "HessianEigenvalue3 (" << std::fixed << std::setprecision(2) << sigma << ")";
 //            result.push_back(std::pair<mitk::Image::Pointer, std::string>(hessianImage3, ss.str()));
 //            ss.str("");
 //        }
     }
 
     return result;
 }
 
-std::pair<std::shared_ptr<LabelVectorType>, std::shared_ptr<FeatureMatrixType>> ActiveLearning::GetTrainingData(const mitk::Image::Pointer annotationImage, const std::vector<mitk::Image::Pointer> featureImageVector)
+std::pair<mitk::Image::Pointer, mitk::Image::Pointer> ActiveLearning::CalculatePrediction(const mitk::Image::Pointer annotationImage,
+                                                                                          const std::vector<mitk::Image::Pointer> &featureImageVector,
+                                                                                          const mitk::Image::Pointer referenceImage,
+                                                                                          mitk::AbstractClassifier* classifier,
+                                                                                          std::shared_ptr<FeatureMatrixType> predictionMatrix)
+{
+    // Create prediction matrix if necessary
+    if (predictionMatrix == nullptr)
+    {
+        FeatureMatrixType mat = Transform<FeaturePixelType>(featureImageVector);
+        predictionMatrix = std::make_shared<FeatureMatrixType>(mat);
+    }
+
+    // Get training data and train
+    auto training = GetTrainingData(annotationImage, featureImageVector);
+    classifier->Train(*training.second, *training.first);
+
+    // Get result
+    LabelVectorType segmentation = classifier->Predict(*predictionMatrix);
+    FeatureMatrixType prediction = classifier->GetPointWiseProbabilities();
+    mitk::Image::Pointer segmentationImage = Transform<LabelPixelType, AnnotationPixelType>(segmentation, referenceImage);
+    mitk::Image::Pointer predictionImage = Transform<FeaturePixelType, FeaturePixelType>(prediction, referenceImage);
+
+    std::pair<mitk::Image::Pointer, mitk::Image::Pointer> result = std::make_pair(segmentationImage, predictionImage);
+    return result;
+}
+
+std::pair<std::shared_ptr<LabelVectorType>, std::shared_ptr<FeatureMatrixType>> ActiveLearning::GetTrainingData(const mitk::Image::Pointer annotationImage,
+                                                                                                                const std::vector<mitk::Image::Pointer> &featureImageVector)
 {
     // Get indices and labels
     std::vector<itk::Index<3>> indices;
     std::vector<LabelPixelType> labels;
     itk::Image<LabelPixelType, 3>::Pointer annotationImageItk;
     mitk::CastToItkImage(annotationImage, annotationImageItk);
 
     itk::ImageRegionIteratorWithIndex<itk::Image<LabelPixelType, 3>> it(annotationImageItk, annotationImageItk->GetLargestPossibleRegion());
     while (!it.IsAtEnd())
     {
         if (it.Get() != 0)
         {
             indices.push_back(it.GetIndex());
             labels.push_back(it.Get());
         }
         ++it;
     }
 
     FeatureMatrixType trainingData(indices.size(), featureImageVector.size());
     LabelVectorType trainingLabels = LabelVectorType::Map(labels.data(), labels.size());
 
     int j = 0;
     for (mitk::Image::Pointer feature : featureImageVector)
     {
         int i = 0;
         mitk::ImagePixelReadAccessor<FeaturePixelType> access(feature, feature->GetVolumeData());
         for (auto index : indices)
         {
             trainingData(i, j) = access.GetPixelByIndexSafe(index);
             i++;
         }
         j++;
     }
 
     auto trainingLabelsPtr = std::make_shared<LabelVectorType>(trainingLabels);
     auto trainingDataPtr = std::make_shared<FeatureMatrixType>(trainingData);
     std::pair<std::shared_ptr<LabelVectorType>, std::shared_ptr<FeatureMatrixType>> result = std::make_pair(trainingLabelsPtr, trainingDataPtr);
 
     return result;
 }
 
 void ActiveLearning::UpdateLookupTables()
 {
     // Create new lookup table from list
     // Annotation type is int, but we only use a ushort lookup table
     auto lut = vtkSmartPointer<vtkLookupTable>::New();
     int lowlim = std::numeric_limits<AnnotationPixelType>::min();
     int uplim = std::numeric_limits<AnnotationPixelType>::max();
     lut->SetNumberOfTableValues(uplim - lowlim + 1);
     lut->SetTableRange(lowlim, uplim);
     for (long i=0; i<(uplim-lowlim+1); ++i)
     {
         lut->SetTableValue(i, 0.0, 0.0, 0.0, 0.0);
     }
     for (int j=0; j<m_LabelListModel->rowCount(); ++j)
     {
         int value = m_LabelListModel->item(j, 1)->text().toInt();
         const QColor color = m_LabelListModel->item(j, 0)->background().color();
         lut->SetTableValue(value, color.redF(), color.greenF(), color.blueF(), 1.0);
     }
 
     auto lutMitk = mitk::LookupTable::New();
     lutMitk->SetVtkLookupTable(lut);
 
     // Set to annotation image and segmentation image
     auto * lut_prop = dynamic_cast<mitk::LookupTableProperty *>(m_AnnotationNode->GetProperty("LookupTable"));
     lut_prop->SetLookupTable(lutMitk);
     m_AnnotationNode->SetProperty("Image Rendering.Mode", mitk::RenderingModeProperty::New(mitk::RenderingModeProperty::LOOKUPTABLE_COLOR));
     lut_prop = dynamic_cast<mitk::LookupTableProperty *>(m_SegmentationNode->GetProperty("LookupTable"));
     lut_prop->SetLookupTable(lutMitk);
     m_SegmentationNode->SetProperty("Image Rendering.Mode", mitk::RenderingModeProperty::New(mitk::RenderingModeProperty::LOOKUPTABLE_COLOR));
 }
 
 const std::string ActiveLearning::VIEW_ID = "org.mitk.views.activelearning";
 
 
 /* ==================================================================
  * PROTECTED SLOTS
  * =============================================================== */
 
 
 void ActiveLearning::OnAddLabelPushButtonClicked()
 {
     QString labelName = QString("Label ") + QString::number(m_LabelListModel->rowCount() + 1);
     QColor labelColor = Qt::GlobalColor(m_LabelListModel->rowCount() % 12 + 7); // We only want Qt default colors 7 to 18
 
     // Create icon
     QStandardItem* colorSquare = new QStandardItem;
     colorSquare->setBackground(labelColor);
     colorSquare->setEditable(false);
     QPixmap colorPixmap(20, 20);
     colorPixmap.fill(labelColor);
     colorSquare->setIcon(QIcon(colorPixmap));
 
     // Key is the highest existing key + 1
     int value = 1;
     if (m_LabelListModel->rowCount() >= 1)
     {
         value = m_LabelListModel->item(m_LabelListModel->rowCount() - 1, 1)->text().toInt() + 1;
     }
     QStandardItem* valueItem = new QStandardItem;
     valueItem->setText(QString::number(value));
 
     // Create label item
     QStandardItem* label = new QStandardItem(labelName);
 
     // Make list and insert
     QList<QStandardItem*> list;
     list.append(colorSquare);
     list.append(valueItem);
     list.append(label);
     m_LabelListModel->appendRow(list);
 
     // If this is the first label, we activate the paint button
     // We also have to set the data node color for this one, because for 1 values that color seems to define the rendered color
     if (m_LabelListModel->rowCount() == 1)
     {
         OnPaintToolButtonClicked();
     }
 
     // Update colors
     UpdateLookupTables();
 }
 
 void ActiveLearning::OnRemoveLabelPushButtonClicked()
 {
     // STILL NEED TO REMOVE ANNOTATIONS
 
     QItemSelectionModel* selection = m_Controls.m_LabelTableView->selectionModel();
     if (selection->hasSelection())
     {
         unsigned int removeIndex = selection->selectedRows().first().row();
         QString removeMessage = QString("Remove label '") + m_LabelListModel->item(removeIndex, 2)->text() + QString("'?");
         QMessageBox::StandardButton removeReply;
         removeReply = QMessageBox::question(m_Parent,
                                             "Remove Label",
                                             removeMessage,
                                             QMessageBox::Yes | QMessageBox::No);
         if (removeReply == QMessageBox::Yes)
         {
             m_LabelListModel->removeRow(removeIndex);
             if (!m_Interactor->IsUsed())
                 ResetLabels();
         }
     }
 }
 
 void ActiveLearning::OnPaintToolButtonClicked()
 {
     m_Controls.m_PaintToolButton->setChecked(true);
     QItemSelectionModel* selection = m_Controls.m_LabelTableView->selectionModel();
     int row(0);
     if (selection->hasSelection())
     {
         row = selection->selectedRows().first().row();
     }
     else
     {
         m_Controls.m_LabelTableView->selectRow(0);
     }
     m_Interactor->SetPaintingPixelValue(m_LabelListModel->item(row, 1)->text().toInt());
 }
 
 void ActiveLearning::OnEraseToolButtonClicked()
 {
     m_Controls.m_EraseToolButton->setChecked(true);
     m_Interactor->SetPaintingPixelValue(0);
 }
 
 void ActiveLearning::OnActivateGuidancePushButtonToggled(bool toggled)
 {
 
 }
 
 void ActiveLearning::OnSaveSegmentationPushButtonClicked()
 {
 
 }
 
 void ActiveLearning::OnSavePredictionsPushButtonClicked()
 {
 
 }
 
 void ActiveLearning::OnExportSegmentationPushButtonClicked()
 {
 
 }
 
 void ActiveLearning::OnExportPredictionsPushButtonClicked()
 {
 
 }
 
 void ActiveLearning::OnColorIconDoubleClicked(const QModelIndex& index)
 {
     // Check if click is really from color icon
     if (index.column() != 0)
     {
         return;
     }
     else
     {
         // Color change dialog
         QColor setColor = QColorDialog::getColor(m_LabelListModel->itemFromIndex(index)->background().color(), m_Parent, "Select Label Color");
         if (setColor.isValid())
         {
             m_LabelListModel->itemFromIndex(index)->setBackground(setColor);
             QPixmap colorPixmap(20, 20);
             colorPixmap.fill(setColor);
             m_LabelListModel->itemFromIndex(index)->setIcon(QIcon(colorPixmap));
             UpdateLookupTables();
         }
     }
 }
 
-void ActiveLearning::OnLabelListSelectionChanged(const QItemSelection& selected, const QItemSelection& /*deselected*/)
+void ActiveLearning::OnLabelListSelectionChanged(const QItemSelection& selected,
+                                                 const QItemSelection& /*deselected*/)
 {
     if (selected.empty()) return;
     if (m_Controls.m_EraseToolButton->isChecked()) return;
 
     // This assumes that only one item can be selected (single selection table view)
     try
     {
         int labelValue = m_LabelListModel->item(selected.indexes()[0].row(), 1)->text().toInt();
         m_Interactor->SetPaintingPixelValue(labelValue);
     }
     catch (...)
     {
         m_Interactor->SetPaintingPixelValue(-1);
     }
 }
 
 void ActiveLearning::OnLabelNameChanged(const QModelIndex& topLeft, const QModelIndex& /*bottomRight*/)
 {
 
 }
 
 void ActiveLearning::OnInitializationFinished()
 {
     // Check if all futures are finished
     for (auto watcher : m_FeatureCalculationWatchers)
     {
         if (watcher->isFinished() == false) {return;}
     }
 
     // Empty feature vector
     m_FeatureImageVector.clear();
 
     // Insert features into feature vector and data storage
     for (unsigned int i=0; i<m_FeatureCalculationWatchers.size(); i++)
     {
         auto result = m_FeatureCalculationWatchers[i]->result();
         for (unsigned int j=0; j<result.size(); j++)
         {
             m_FeatureImageVector.push_back(result[j].first);
             auto node = mitk::DataNode::New();
             node->SetData(result[j].first);
             node->SetName(result[j].second);
             node->SetBoolProperty("helper object", true);
             node->SetVisibility(false);
             this->GetDataStorage()->Add(node, m_Nodes[i]);
         }
     }
 
     // Show controls
     m_Controls.m_LabelControlsFrame->setVisible(true);
     m_Controls.m_InitializePushButton->setHidden(true);
 
     // Delete watchers
     for (auto watcher : m_FeatureCalculationWatchers)
     {
         delete watcher;
     }
     m_FeatureCalculationWatchers.clear();
 }
 
 void ActiveLearning::OnUpdatePredictionsPushButtonClicked()
 {
-    if (m_PredictionMatrix == nullptr)
-    {
-        FeatureMatrixType mat = Transform<FeaturePixelType>(m_FeatureImageVector);
-        m_PredictionMatrix = std::make_shared<FeatureMatrixType>(mat);
-    }
+    m_Controls.m_UpdatePredictionsPushButton->setDisabled(true);
 
     // Classifier
     m_Classifier = mitk::VigraRandomForestClassifier::New();
     m_Classifier->SetTreeCount(m_NumberOfTrees);
     m_Classifier->SetMaximumTreeDepth(m_MaximumTreeDepth);
     m_Classifier->SetSamplesPerTree(m_SamplesPerTree);
 
-    auto training = GetTrainingData(m_AnnotationImage, m_FeatureImageVector);
-    m_Classifier->Train(*training.second, *training.first);
-
-    LabelVectorType prediction = m_Classifier->Predict(*m_PredictionMatrix);
     mitk::Image::Pointer referenceImage = dynamic_cast<mitk::Image*>(m_Nodes[0]->GetData());
+    QFuture<std::pair<mitk::Image::Pointer, mitk::Image::Pointer>> future;
+    future = QtConcurrent::run(this, &ActiveLearning::CalculatePrediction, m_AnnotationImage, m_FeatureImageVector, referenceImage, m_Classifier, m_PredictionMatrix);
+    m_PredictionCalculationWatcher = new QFutureWatcher<std::pair<mitk::Image::Pointer, mitk::Image::Pointer>>();
+    m_PredictionCalculationWatcher->setFuture(future);
 
-    m_SegmentationImage = Transform<LabelPixelType, AnnotationPixelType>(prediction, referenceImage);
-    m_SegmentationImage->Modified();
-    m_SegmentationNode->SetData(m_SegmentationImage);
-    m_SegmentationNode->Modified();
-
-//    this->RequestRenderWindowUpdate();
+    connect(m_PredictionCalculationWatcher, SIGNAL(finished()),
+            this, SLOT(OnPredictionCalculationFinished()));
 }
 
 void ActiveLearning::OnPredictionCalculationFinished()
 {
+    auto result = m_PredictionCalculationWatcher->result();
+
+    m_SegmentationImage = result.first;
+    m_SegmentationImage->Modified();
+    m_SegmentationNode->SetData(m_SegmentationImage);
+    m_SegmentationNode->Modified();
+
+    m_PredictionImage = result.second;
+    m_PredictionImage->Modified();
+    m_PredictionNode->SetData(m_PredictionImage);
+    m_PredictionNode->Modified();
 
+    m_Controls.m_UpdatePredictionsPushButton->setEnabled(true);
 }
 
 /* ==================================================================
  * PROTECTED
  * =============================================================== */
 
 
 void ActiveLearning::OnSelectionChanged(berry::IWorkbenchPart::Pointer /*source*/,
                                         const QList<mitk::DataNode::Pointer>& nodes)
 {
     if (!SelectionAllImages(nodes))
     {
         SetInitializeReady(false);
         return;
     }
 
     if (nodes.length() >= 2)
     {
         // First selection is the reference (could be any other)
         mitk::Image::Pointer referenceImage = dynamic_cast<mitk::Image*>(nodes[0]->GetData());
         mitk::BaseGeometry* referenceGeometry = referenceImage->GetTimeGeometry()->GetGeometryForTimeStep(0); // Adjust for multiple timesteps
 
         for (int i=1; i<nodes.length(); ++i)
         {
             mitk::Image::Pointer currentImage = dynamic_cast<mitk::Image*>(nodes[i]->GetData());
             mitk::BaseGeometry* currentGeometry = currentImage->GetTimeGeometry()->GetGeometryForTimeStep(0); // Adjust for multiple timesteps
 
             if (!mitk::Equal(*currentGeometry, *referenceGeometry, mitk::eps, true))
             {
                 SetInitializeReady(false);
                 return;
             }
         }
     }
 
     // All nodes have the same geometry, allow init
     SetInitializeReady(true);
 }
 
 void ActiveLearning::SetFocus()
 {
 }
 
 
 /* ==================================================================
  * PRIVATE
  * =============================================================== */
 
 
 void ActiveLearning::SetInitializeReady(bool ready)
 {
     if (ready)
     {
         // get selection, check again just to be sure
         auto nodes = this->GetDataManagerSelection();
         if (!SelectionAllImages(nodes)) return;
 
         m_Controls.m_InitializePushButton->setEnabled(true);
 
         if (!m_Active)
         {
             QString nameList = QString::fromStdString(nodes[0]->GetName());
             if (nodes.length() >= 2)
             {
                 for (int i=1; i<nodes.length(); ++i)
                 {
                     nameList += QString("<br />");
                     nameList += QString::fromStdString(nodes[i]->GetName());
                 }
             }
             m_Controls.m_InitializeLabel->setText(nameList);
         }
     }
     else
     {
         m_Controls.m_InitializePushButton->setDisabled(true);
         if (!m_Active)
         {
             m_Controls.m_InitializeLabel->setText("<font color='red'>Selected images must have matching geometries</font>");
 
         }
     }
 }
diff --git a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h
index 0f1791583a..04dd476249 100644
--- a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h
+++ b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h
@@ -1,172 +1,177 @@
 /*===================================================================
 
 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 ActiveLearning_h
 #define ActiveLearning_h
 
 #include <berryISelectionListener.h>
 #include <QmitkAbstractView.h>
 #include "ui_QmitkActiveLearningControls.h"
 
 // Qt
 #include <QItemDelegate>
 #include <QStandardItemModel>
 #include <QWidget>
 #include <QFutureWatcher>
 
 // MITK
 #include <mitkLabelSetImage.h>
 #include <mitkActiveLearningInteractor.h>
 #include <mitkVigraRandomForestClassifier.h>
 
 #include <Eigen/Dense>
 
 /**
 \brief ActiveLearning
 
 \warning  This class is not yet documented. Use "git blame" and ask the author to provide basic documentation.
 
 \sa QmitkAbstractView
 \ingroup ${plugin_target}_internal
 */
 
+
+
 // Just a helper class
 class NotEditableDelegate : public QItemDelegate
 {
     Q_OBJECT
 public:
     explicit NotEditableDelegate(QObject* parent = nullptr) : QItemDelegate(parent) {}
 protected:
     QWidget* createEditor(QWidget*, const QStyleOptionViewItem&, const QModelIndex&) const {return Q_NULLPTR;}
 };
 
+
+
 class ActiveLearning : public QmitkAbstractView
 {
 
     Q_OBJECT
 
 public slots:
 
     void Initialize();
 
 public:
 
     typedef unsigned short AnnotationPixelType;
     typedef double FeaturePixelType;
     typedef int LabelPixelType;
     typedef Eigen::Matrix<FeaturePixelType, Eigen::Dynamic, Eigen::Dynamic> FeatureMatrixType;
     typedef Eigen::Matrix<LabelPixelType, Eigen::Dynamic, 1> LabelVectorType;
 
     ActiveLearning();
     ~ActiveLearning();
 
     void CreateQtPartControl(QWidget *parent) override;
 
     std::vector<std::pair<mitk::Image::Pointer, std::string>> CalculateFeatures(const mitk::Image::Pointer inputImage);
 
     std::pair<mitk::Image::Pointer, mitk::Image::Pointer> CalculatePrediction(const mitk::Image::Pointer annotationImage,
                                                                               const std::vector<mitk::Image::Pointer> &featureImageVector,
                                                                               const mitk::Image::Pointer referenceImage,
+                                                                              mitk::AbstractClassifier *classifier,
                                                                               std::shared_ptr<FeatureMatrixType> predictionMatrix);
 
     std::pair<std::shared_ptr<LabelVectorType>, std::shared_ptr<FeatureMatrixType> > GetTrainingData(const mitk::Image::Pointer annotationImage,
                                                                                                      const std::vector<mitk::Image::Pointer> &featureImageVector);
 
     void UpdateLookupTables();
 
     static const std::string VIEW_ID;
 
 protected slots:
 
     void OnAddLabelPushButtonClicked();
 
     void OnRemoveLabelPushButtonClicked();
 
     void OnPaintToolButtonClicked();
 
     void OnEraseToolButtonClicked();
 
     void OnActivateGuidancePushButtonToggled(bool toggled);
 
     void OnSaveSegmentationPushButtonClicked();
 
     void OnSavePredictionsPushButtonClicked();
 
     void OnExportSegmentationPushButtonClicked();
 
     void OnExportPredictionsPushButtonClicked();
 
     void OnColorIconDoubleClicked(const QModelIndex& index);
 
     void OnLabelListSelectionChanged(const QItemSelection& selected, const QItemSelection& /*deselected*/);
 
     void OnLabelNameChanged(const QModelIndex& topLeft, const QModelIndex& /*bottomRight*/);
 
     void OnUpdatePredictionsPushButtonClicked();
 
     void OnPredictionCalculationFinished();
 
     void OnInitializationFinished();
 
 protected:
 
     void OnSelectionChanged(berry::IWorkbenchPart::Pointer /*source*/,
                             const QList<mitk::DataNode::Pointer>& nodes) override;
 
     void SetFocus() override;
 
     void ResetLabels();
 
     void SetInitializeReady(bool ready);
 
     Ui::ActiveLearningControls m_Controls;
     QWidget* m_Parent;
 
     mitk::Image::Pointer m_AnnotationImage;
     mitk::DataNode::Pointer m_AnnotationNode;
 
     mitk::Image::Pointer m_PredictionImage;
     mitk::DataNode::Pointer m_PredictionNode;
 
     mitk::Image::Pointer m_SegmentationImage;
     mitk::DataNode::Pointer m_SegmentationNode;
 
     std::vector<mitk::Image::Pointer> m_FeatureImageVector;
     std::vector<QFutureWatcher<std::vector<std::pair<mitk::Image::Pointer, std::string>>>*> m_FeatureCalculationWatchers;
 
-    QFutureWatcher<mitk::Image::Pointer>* m_PredictionCalculationWatcher;
+    QFutureWatcher<std::pair<mitk::Image::Pointer, mitk::Image::Pointer>>* m_PredictionCalculationWatcher;
 
     QStandardItemModel* m_LabelListModel;
 
     mitk::ActiveLearningInteractor::Pointer m_Interactor;
 
     QList<mitk::DataNode::Pointer> m_Nodes;
 
     mitk::VigraRandomForestClassifier::Pointer m_Classifier;
 
 private:
 
     bool m_Active;
 
     int m_NumberOfTrees;
     int m_MaximumTreeDepth;
     float m_SamplesPerTree;
 
     std::shared_ptr<FeatureMatrixType> m_PredictionMatrix;
 
 };
 
 #endif // ActiveLearning_h