Change Details

Advanced setting flags like: * --fast: For faster runtime and less memory requirements use this option. It will run a lower resolution model (3mm instead of 1.5mm). * --preview: This will generate a 3D rendering of all classes, giving you a quick overview if the segmentation worked and where it failed (see preview.png in output directory). * --ml: This will save one nifti file containing all labels instead of one file for each class. Saves runtime during saving of nifti files. * --roi_subset: Takes a space separated list of class names (e.g. spleen colon brain) and only saves those classes. Saves runtime during saving of nifti files. * --statistics: This will generate a file statistics.json with volume (in mm³) and mean intensity of each class. * --radiomics: This will generate a file statistics_radiomics.json with radiomics features of each class. You have to install pyradiomics to use this (pip install pyradiomics). exist for TotalSegmentator. This can be also included in the MITK workflow after the basic segmentation workflow is working. (T29457)

Advanced setting flags like: * --fast: For faster runtime and less memory requirements use this option. It will run a lower resolution model (3mm instead of 1.5mm). * --preview: This will generate a 3D rendering of all classes, giving you a quick overview if the segmentation worked and where it failed (see preview.png in output directory). * --ml: This will save one nifti file containing all labels instead of one file for each class. Saves runtime during saving of nifti files. * --roi_subset: Takes a space separated list of class names (e.g. spleen colon brain) and only saves those classes. Saves runtime during saving of nifti files. * --statistics: This will generate a file statistics.json with volume (in mm³) and mean intensity of each class. * --radiomics: This will generate a file statistics_radiomics.json with radiomics features of each class. You have to install pyradiomics to use this (pip install pyradiomics). exist for TotalSegmentator. Some of these could be potentially also included in the MITK along with GPU selection options (like in the nnUNet tool) in the tool workflow after the basic segmentation workflow is working. (T29457)

Advanced setting flags like: * --fast: For faster runtime and less memory requirements use this option. It will run a lower resolution model (3mm instead of 1.5mm). * --preview: This will generate a 3D rendering of all classes, giving you a quick overview if the segmentation worked and where it failed (see preview.png in output directory). * --ml: This will save one nifti file containing all labels instead of one file for each class. Saves runtime during saving of nifti files. * --roi_subset: Takes a space separated list of class names (e.g. spleen colon brain) and only saves those classes. Saves runtime during saving of nifti files. * --statistics: This will generate a file statistics.json with volume (in mm³) and mean intensity of each class. * --radiomics: This will generate a file statistics_radiomics.json with radiomics features of each class. You have to install pyradiomics to use this (pip install pyradiomics). exist for TotalSegmentator. This can beSome of these could be potentially also included in the MITK along with GPU selection options (like in the nnUNet tool) in the tool workflow after the basic segmentation workflow is working. (T29457)