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Introduction and Acknowledgements

This work is supported by NA-MIC, NCIGT, and the Slicer Community.
Author: Vivek Narayan, Jayender Jagadeesan
Contact: Jayender Jagadeesan <email></email>


This project is supported by P41 RR019703/RR/NCRR NIH HHS/United States, P01 CA067165/CA/NCI NIH HHS/United States and P41 EB015898/EB/NIBIB NIH HHS/United States


Extension Description

  • The SegmentCAD module is designed to segment tumors from DCE-MRI datasets which include a pre-contrast image and post-contrast images at different time points.
    • SegmentCAD uses blackbox methods to calculate the wash-in and wash-out slopes from the time-intensity curves.
    • The segmentation output is a Label Map with red, yellow, and blue colors respectively identifying washout (Type III), plateau (Type II), and persistent (Type I) voxels.
  • The HeterogeneityCAD module is an extensible, image feature extraction toolbox primarily to quantify the heterogeneity of tumor images and their label maps.
    • Metrics have been implemented from a variety of feature classes including:
      • First-Order/Histogram statistics
      • Morphology/Shape measures and Geometrical (4D Extrusion) measures
      • Renyi/Fractal dimensions
      • Texture features computed from Gray-Level Co-occurrence Matrices (GLCM) and from Gray-Level Run Length matrices (GLRL)


Data sets

Quick Instructions for Use

  • SegmentCAD (Click link for detailed description)
    • Select the pre-contrast volume
    • Select the first post-contrast volume
    • Select the second post-contrast volume
    • Select the third post-contrast volume
    • Select the fourth post-contrast volume
    • Create or select a label map volume node to represent the output of the segmentation
    • Click "Apply OpenCAD Segmentation"
  • HeterogeneityCAD (Click link for detailed description)
    • Add an image or parameter map (.nrrd file) to the Nodes List
    • Select a corresponding segmentation label map to use as ROI
    • Click "Apply HeterogeneityCAD"

Similar Modules

  • SegmentCAD:
  • HeterogeneityCAD:
    • LabelStatistics


  • J. Jayender, E. Gombos, S. Chikarmane, D. Dabydeen, F. A. Jolesz, and K. G. Vosburgh, “Statistical Learning Algorithm for In-situ and Invasive Breast Carcinoma Segmentation”, Journal of Computerized Medical Imaging and Graphics, vol. 37, no. 4, pp. 281-292, 2013
  • J. Jayender, S. A. Chikarmane, F. A. Jolesz and E. Gombos, “Automatic Segmentation of Invasive Breast Carcinomas from DCE-MRI using Time Series Analysis”, Journal of MRI, Article first published online 23 September 2013, doi: 10.1002/jmri.24394
  • J. Jayender, K.G. Vosburgh, E. Gombos, A. Ashraf, D. Kontos, S.C. Gavenonis, F. A. Jolesz and K. Pohl , “Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm”, IEEE International Symposium on Biomedical Imaging, pp. 122-125, 2012.
  • J. Jayender, D.T. Ruan, V. Narayan, N. Agrawal, F. A. Jolesz and H. Mamata, “Segmentation of Parathyroid Tumors from DCE-MRI using Linear Dynamic System Analysis”, IEEE International Symposium on Biomedical Imaging, 2013.
  • J. Jayender, J. Jagannathan, S.Chikarmane, C.P.Raut and F.A. Jolesz, “Computer-Aided Diagnosis of Breast Angiosarcoma: Results in 14 cases”, Quantitative Medical Imaging Symposium, 2013 (invited paper).
  • HJWL Aerts, ER Velazquez, RTH Leijenaar, et al., "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach", vol. 5, Nat Communication, 2014.

Information for Developers

Source code: