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 6a06bb8740..d3f2c8a787 100644
--- a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp
+++ b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp
@@ -1,640 +1,733 @@
 /*===================================================================
 
 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"
 #include <QmitkLabelSetWidget.h>
 
 // MITK
 #include <usModuleRegistry.h>
 #include <mitkImage.h>
 #include <mitkImageWriteAccessor.h>
 #include <mitkLabel.h>
 #include <mitkBaseGeometry.h>
 #include <mitkNumericTypes.h>
 #include <mitkPixelType.h>
 #include <mitkImageCast.h>
+#include <mitkImageAccessByItk.h>
 
 // ITK
 #include <itkDiscreteGaussianImageFilter.h>
+#include <itkGradientMagnitudeRecursiveGaussianImageFilter.h>
+#include <itkLaplacianRecursiveGaussianImageFilter.h>
 #include <itkHessianMatrixEigenvalueImageFilter.h>
 #include <itkStructureTensorEigenvalueImageFilter.h>
 
 
 // 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);
     }
 }
 
+/* ==================================================================
+ * FEATURES
+ * =============================================================== */
+
+template<typename PixelType, unsigned int imageDimension>
+static void GaussianSmoothing(const itk::Image<PixelType, imageDimension>* inputImage, float sigma, mitk::Image::Pointer outputImage)
+{
+    typedef itk::Image<PixelType, imageDimension> ImageType;
+    auto filter = itk::DiscreteGaussianImageFilter<ImageType, ImageType>::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, float sigma, mitk::Image::Pointer outputImage)
+{
+    typedef itk::Image<PixelType, imageDimension> ImageType;
+    auto filter = itk::GradientMagnitudeRecursiveGaussianImageFilter<ImageType, ImageType>::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, float sigma, mitk::Image::Pointer outputImage)
+{
+    typedef itk::Image<PixelType, imageDimension> ImageType;
+    auto filter = itk::LaplacianRecursiveGaussianImageFilter<ImageType, ImageType>::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)
+{
+    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)
+{
+    typedef itk::Image<PixelType, imageDimension> ImageType;
+    auto filter = itk::HessianMatrixEigenvalueImageFilter<ImageType>::New();
+    filter->SetInput(inputImage);
+    filter->SetSigma(sigma);
+    filter->Update();
+    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
-    auto nodes = this->GetDataManagerSelection();
-    if (!SelectionAllImages(nodes)) return;
+    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(nodes[0]->GetName());
-    if (nodes.length() >= 2)
+    QString nameList = QString::fromStdString(m_Nodes[0]->GetName());
+    if (m_Nodes.length() >= 2)
     {
-        for (int i=1; i<nodes.length(); ++i)
+        for (int i=1; i<m_Nodes.length(); ++i)
         {
             nameList += QString("<br />");
-            nameList += QString::fromStdString(nodes[i]->GetName());
+            nameList += QString::fromStdString(m_Nodes[i]->GetName());
         }
     }
     m_Controls.m_InitializeLabel->setText(nameList);
 
     // =======================================
     // ANNOTATION IMAGE
     // =======================================
 
     m_AnnotationImage = mitk::LabelSetImage::New();
 
     try
     {
-        auto referenceImage = dynamic_cast<mitk::Image*>(nodes[0]->GetData());
+        auto referenceImage = dynamic_cast<mitk::Image*>(m_Nodes[0]->GetData());
         m_AnnotationImage->Initialize(referenceImage);
     }
     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("helper object", true);
-    m_AnnotationImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(nodes[0]->GetName().c_str()));
+    m_AnnotationImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(m_Nodes[0]->GetName().c_str()));
     m_AnnotationImage->GetExteriorLabel()->SetProperty("name.image", mitk::StringProperty::New("Labels"));
-    this->GetDataStorage()->Add(m_AnnotationNode, nodes[0]);
+    this->GetDataStorage()->Add(m_AnnotationNode, m_Nodes[0]);
 
     // =======================================
     // PREDICTION IMAGE
     // =======================================
 
     m_PredictionImage = mitk::Image::New();
 
     try
     {
-        mitk::Image::Pointer referenceImage = dynamic_cast<mitk::Image*>(nodes[0]->GetData());
+        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("helper object", true);
 //    m_PredictionImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(nodes[0]->GetName().c_str()));
 //    m_PredictionImage->GetExteriorLabel()->SetProperty("name.image", mitk::StringProperty::New("Predictions"));
-    this->GetDataStorage()->Add(m_PredictionNode, nodes[0]);
+    this->GetDataStorage()->Add(m_PredictionNode, m_Nodes[0]);
 
     // =======================================
     // SEGMENTATION IMAGE
     // =======================================
 
     m_SegmentationImage = mitk::LabelSetImage::New();
 
     try
     {
-        auto referenceImage = dynamic_cast<mitk::Image*>(nodes[0]->GetData());
+        auto referenceImage = dynamic_cast<mitk::Image*>(m_Nodes[0]->GetData());
         m_SegmentationImage->Initialize(referenceImage);
     }
     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_PredictionNode->SetBoolProperty("helper object", true);
-    m_SegmentationImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(nodes[0]->GetName().c_str()));
+    m_SegmentationImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(m_Nodes[0]->GetName().c_str()));
     m_SegmentationImage->GetExteriorLabel()->SetProperty("name.image", mitk::StringProperty::New("Segmentation"));
-    this->GetDataStorage()->Add(m_SegmentationNode, nodes[0]);
+    this->GetDataStorage()->Add(m_SegmentationNode, m_Nodes[0]);
 
     // Calculate features
-    std::vector<mitk::Image::Pointer> rawImages;
-    for (const auto& node : nodes)
+    for (const auto node : m_Nodes)
     {
-        rawImages.push_back(dynamic_cast<mitk::Image*>(node->GetData()));
+        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);
     }
-    m_FeatureCalculationFuture = QtConcurrent::run(this, &ActiveLearning::CalculateFeatures, rawImages);
-    m_FeatureCalculationWatcher.setFuture(m_FeatureCalculationFuture);
 
     // 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_Controls.m_LabelControlsFrame->setVisible(true);
     m_Controls.m_InitializePushButton->setHidden(true);
 
     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)
 {
 
 }
 
 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()));
 
-    // Future watchers
-    connect(&m_FeatureCalculationWatcher, SIGNAL(finished()),
-            this, SLOT(OnInitializationFinished()));
-
     // Set start configuration
     m_Controls.m_LabelControlsFrame->setVisible(false);
     SetInitializeReady(false);
 }
 
 void ActiveLearning::ResetLabels()
 {
     // Remove all labels but the first
     for (auto it=m_AnnotationImage->GetActiveLabelSet()->IteratorBegin(); it!=m_AnnotationImage->GetActiveLabelSet()->IteratorConstEnd(); it++)
     {
         if (it->first != 0)
         {
             m_AnnotationImage->GetActiveLabelSet()->RemoveLabel(it->first);
         }
     }
 
     // Fill with labels from list
     for (int i=0; i<m_LabelListModel->rowCount(); i++)
     {
         QString name = m_LabelListModel->item(i, 2)->text();
         m_LabelListModel->item(i, 1)->setText(QString::number(i + 1));
         QColor color = m_LabelListModel->item(i)->background().color();
         m_AnnotationImage->GetActiveLabelSet()->AddLabel(name.toStdString(), QColorToMitkColor(color));
     }
 }
 
-std::vector<mitk::Image::Pointer> ActiveLearning::CalculateFeatures(std::vector<mitk::Image::Pointer> inputVector)
+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};
 
-    const int dim = inputVector[0]->GetDimension();
-    typedef itk::Image<float, 3> FeatureImageType;
-    std::vector<FeatureImageType::Pointer> rawVectorITK;
-    std::vector<mitk::Image::Pointer> outputVector;
-
-    // Cast input images to float
-    for (auto it = inputVector.begin(); it != inputVector.end(); it++)
+    for (auto sigma : sigmas)
     {
-        FeatureImageType::Pointer currentITK;
-        mitk::Image::Pointer currentMITK;
-        mitk::CastToItkImage(*it, currentITK);
-        rawVectorITK.push_back(currentITK);
-        mitk::CastToMitkImage(currentITK, currentMITK);
-        outputVector.push_back(currentMITK);
+        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("");
     }
 
-    // Gaussian Smoothing
-    auto gaussianSmoothing = itk::DiscreteGaussianImageFilter<FeatureImageType, FeatureImageType>::New();
-    for (auto image = rawVectorITK.begin(); image != rawVectorITK.end(); image++)
-    {
-        for (auto sigma = sigmas.begin(); sigma != sigmas.end(); sigma++)
-        {
-            gaussianSmoothing->SetInput(*image);
-            gaussianSmoothing->SetVariance(*sigma);
-            gaussianSmoothing->Update();
-            mitk::Image::Pointer currentImage;
-            mitk::CastToMitkImage(gaussianSmoothing->GetOutput(), currentImage);
-            outputVector.push_back(currentImage);
-        }
-    }
-
-    return outputVector;
+    return result;
 }
 
 const std::string ActiveLearning::VIEW_ID = "org.mitk.views.activelearning";
 
 
 /* ==================================================================
  * PROTECTED SLOTS
  * =============================================================== */
 
 
 void ActiveLearning::OnAddLabelPushButtonClicked()
 {
     QString labelName = QString("Label ") + QString::number(m_AnnotationImage->GetActiveLabelSet()->ReverseIteratorBegin()->first + 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 = (int)m_AnnotationImage->GetActiveLabelSet()->ReverseIteratorBegin()->first + 1;
     QStandardItem* valueItem = new QStandardItem();
     valueItem->setText(QString::number(value));
 
     // Create label item
     QStandardItem* label = new QStandardItem(labelName);
 
     // Add to image
     m_AnnotationImage->GetActiveLabelSet()->AddLabel(labelName.toStdString(), QColorToMitkColor(labelColor));
     PrintAllLabels(m_AnnotationImage);
 
     // 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();
         m_AnnotationNode->SetColor(QColorToMitkColor(labelColor));
     }
 
     // Select newly added label
     m_Controls.m_LabelTableView->selectRow(m_LabelListModel->rowCount() - 1);
 }
 
 void ActiveLearning::OnRemoveLabelPushButtonClicked()
 {
     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)
         {
             // if there are no annotations, reset labels
             if (m_Interactor->IsUsed())
             {
                 std::vector<mitk::LabelSetImage::PixelType> labels;
                 labels.push_back(m_LabelListModel->item(removeIndex, 1)->text().toInt());
                 m_AnnotationImage->RemoveLabels(labels);
                 m_LabelListModel->removeRow(removeIndex);
             }
             else
             {
                 m_LabelListModel->removeRow(removeIndex);
                 ResetLabels();
             }
             PrintAllLabels(m_AnnotationImage);
         }
     }
 }
 
 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());
     m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(m_LabelListModel->item(row, 1)->text().toInt());
 }
 
 void ActiveLearning::OnEraseToolButtonClicked()
 {
     m_Controls.m_EraseToolButton->setChecked(true);
     m_Interactor->SetPaintingPixelValue(0);
     m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(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));
 
             // Set color on label
             m_AnnotationImage->GetActiveLabelSet()->GetLabel(m_LabelListModel->item(index.row(), 1)->text().toInt())->SetColor(QColorToMitkColor(setColor));
             m_AnnotationImage->GetActiveLabelSet()->UpdateLookupTable(m_LabelListModel->item(index.row(), 1)->text().toInt());
             PrintAllLabels(m_AnnotationImage);
 
             // If this is the label with value 1 we have to change the data node color
             if (m_LabelListModel->item(index.row(), 1)->text().toInt() == 1)
             {
                 m_AnnotationNode->SetColor(QColorToMitkColor(setColor));
             }
         }
     }
 }
 
 void ActiveLearning::OnLabelListSelectionChanged(const QItemSelection& selected, const QItemSelection& /*deselected*/)
 {
     if (selected.empty()) 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);
         m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(labelValue);
     }
     catch (...)
     {
         m_Interactor->SetPaintingPixelValue(0);
         m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(0);
     }
 }
 
 void ActiveLearning::OnLabelNameChanged(const QModelIndex& topLeft, const QModelIndex& /*bottomRight*/)
 {
     auto item = m_LabelListModel->itemFromIndex(topLeft);
     if (item->column() != 2) return;
     m_AnnotationImage->GetActiveLabelSet()->GetLabel(m_LabelListModel->item(item->row(), 1)->text().toInt())->SetName(item->text().toStdString());
 }
 
 void ActiveLearning::OnInitializationFinished()
 {
-    m_FeatureImageVector = m_FeatureCalculationWatcher.result();
+    // 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);
     MITK_INFO << "Features: " << m_FeatureImageVector.size();
+
+    // Delete watchers
+    for (auto watcher : m_FeatureCalculationWatchers)
+    {
+        delete watcher;
+    }
+    m_FeatureCalculationWatchers.clear();
 }
 
 /* ==================================================================
  * 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 d40a37cda7..7db3301237 100644
--- a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h
+++ b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h
@@ -1,137 +1,137 @@
 /*===================================================================
 
 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>
 
 /**
 \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:
 
     ActiveLearning();
     ~ActiveLearning();
 
     void CreateQtPartControl(QWidget *parent) override;
 
     void ResetLabels();
 
-    std::vector<mitk::Image::Pointer> CalculateFeatures(std::vector<mitk::Image::Pointer> inputVector);
+    std::vector<std::pair<mitk::Image::Pointer, std::string>> CalculateFeatures(const mitk::Image::Pointer inputImage);
 
     static const std::string VIEW_ID;
     QStandardItemModel* m_LabelListModel;
 
 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 OnInitializationFinished();
 
 protected:
 
     void OnSelectionChanged(berry::IWorkbenchPart::Pointer /*source*/,
                             const QList<mitk::DataNode::Pointer>& nodes) override;
 
     void SetFocus() override;
 
     Ui::ActiveLearningControls m_Controls;
     QWidget* m_Parent;
 
     mitk::LabelSetImage::Pointer m_AnnotationImage;
     mitk::DataNode::Pointer m_AnnotationNode;
 
     mitk::Image::Pointer m_PredictionImage;
     mitk::DataNode::Pointer m_PredictionNode;
 
     mitk::LabelSetImage::Pointer m_SegmentationImage;
     mitk::DataNode::Pointer m_SegmentationNode;
 
     std::vector<mitk::Image::Pointer> m_FeatureImageVector;
 
-    QFuture<std::vector<mitk::Image::Pointer>> m_FeatureCalculationFuture;
-    QFutureWatcher<std::vector<mitk::Image::Pointer>> m_FeatureCalculationWatcher;
+    std::vector<QFutureWatcher<std::vector<std::pair<mitk::Image::Pointer, std::string>>>*> m_FeatureCalculationWatchers;
 
 private:
 
     void SetInitializeReady(bool ready);
 
     bool m_Active;
     mitk::ActiveLearningInteractor::Pointer m_Interactor;
+    QList<mitk::DataNode::Pointer> m_Nodes;
 
 };
 
 #endif // ActiveLearning_h