Cancer Imaging Phenomics Toolkit (CaPTk)  1.6.1

Geodesic Distance Transform-based Segmentation

The geodesic distance transform based segmentation is a semi-automatic technique to delineate structures of distinct intensity.

REQUIREMENTS: A single image with distinct boundaries for the structure that needs to be segmented [1].


  1. Load in CaPTk the image that you want to segment.
  2. Using Label 1 from the drawing tab, annotate a region of the tissue you would like to segment in the image.
  3. Launch the application using the 'Applications' -> 'Geodesic Segmentation' menu option.
  4. The mask is populated within ~5 minutes, showing the progress at the bottom right corner of CaPTk.
  5. The mask is visualized automatically in the visualization panels.
  6. You can revise the resulted segmentation mask (Label:1), by selecting the "Geodesic" preset and changing the "Threshold" at the bottom right corner of CaPTk.
  • This application is also available as with a stand-alone CLI for data analysts to build pipelines around, and can run in the following format:
      GeodesicSegmentation.exe -i C:/inputImage.nii.gz -m C:/maskWithOneLabel.nii.gz -o C:/outputImage.nii.gz -t 20


ITK-SNAP is a stand-alone software application used to segment structures in 3D medical images and other utilities [2] -

Within CaPTk specifically, ITK-SNAP is tightly integrated as a tool used for segmentation, accepting files chosen through the CaPTk interface and returning results for further use within CaPTk. ITK-SNAP uses a combination of random forests and level sets to obtain precise segmentations of structures [2]. Please see the following video for detailed instructions:


  1. B.Gaonkar, L.Shu, G.Hermosillo, Y.Zhan, "Adaptive geodesic transform for segmentation of vertebrae on CT images", Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 9035:16, 2014. DOI:10.1117/12.2043527.
  2. P.Yushkevich, Y.Gao, G.Gerig, "ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images", Conf Proc IEEE Eng Med Biol Soc. 2016:3342-3345, 2016. DOI:10.1109/EMBC.2016.7591443.

DeepMedic (Windows-only)

DeepMedic is a Deep Learning based segmentation algorithm [1,2] and users can do inference using a pre-trained model (trained on BraTS 2017 Training Data) with CaPTk for Brain Tumor Segmentation.

REQUIREMENTS: The 4 basic MRI modalities (T1, T1-Gd, T2 and T2-FLAIR) for a subject which are co-registered.


  1. Load in CaPTk the images that you want to segment.
  2. [OPTIONAL] Load the brain mask - this is used for normalization.
  3. Select the output folder.
  4. Click on 'Applications' -> 'DeepMedic Segmentation'

This can also be used from the command line:

DeepMedic.exe -t1 C:/data/t1.nii.gz -t2 C:/data/t2.nii.gz -t1c C:/data/t1ce.nii.gz -fl C:/data/fl.nii.gz -o C:/data/output/ -m C:/data/optionalMask.nii.gz


  1. K.Kamnitsas, C.Ledig, V.F.J.Newcombe, J.P.Simpson, A.D.Kane, D.K.Menon, D.Rueckert, B.Glocker, "Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation", Medical Image Analysis, 2016.
  2. K.Kamnitsas, L.Chen, C.Ledig, D.Rueckert, B.Glocker, "Multi-Scale 3D CNNs for segmentation of brain Lesions in multi-modal MRI", in proceeding of ISLES challenge, MICCAI 2015.