Available Software

 

 

This page lists publicly available software (under construction)
 

Knee Segmentation and Registration Toolkit (KSRT)

KSRT is an open source software distribution of algorithms to automatically quantify cartilage thickness from magnetic resonance (MR) images and to perform related statistical analyses. The implemented algorithms are described in more detail here .

A particularly nice overview of the methods and results is provided in Liang Shan's PhD defense slides.

In particular, the software allows to automatically perform the following analysis tasks:

  1. Segmenting cartilage from knee Magnetic Resonance Imaging (MRI) data.
  2. Computing cartilage thickness from the automatic cartilage segmentations.
  3. Establishing spatial correspondence across MRI data appropriate for statistical analysis.
  4. Performing statistical analysis of localized cartilage changes for cross-sectional and longitudinal data.

A software manual can be downloaded as a [PDF] or accessed through [HTML].

The software itself is available on bitbucket and can be checked out using git

git clone git@bitbucket.org:marcniethammer/ksrt.git

This work was supported by NIH grant: R21AR059890: Automatic Quantitative Analysis of MR images of the knee in osteoarthritis.

 

Related references are: [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]


References

  1. Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models,
    Niethammer, M., Pohl K.  M., Janoos F., and Wells, III W.  M.
    , ArXiv e-prints; accepted to SIAM Journal on Imaging Science, (2017)
  2. Automatic localized analysis of longitudinal cartilage changes,
    Shan, Liang
    , (2014)
  3. Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images,
    Shan, L., Zach C., Charles C., and Niethammer M.
    , Medical Image Analysis, (2014)
  4. Diseased Region Detection of Longitudinal Knee MRI Data,
    Huang, C., Shan L., Charles C., Niethammer M., and Zhu H.
    , Proceedings of the Conference on Information Processing in Medical Imaging (IPMI), Volume 7917, p.632–643, (2013)
  5. Longitudinal three-label segmentation of knee cartilage,
    Shan, L., Charles C., and Niethammer M.
    , Proceedings of the International Symposium on Biomedical Imaging (ISBI), (2013)
  6. Automatic Multi-Atlas-Based Cartilage Segmentation from Knee MR Images,
    Shan, L., Charles C., and Niethammer M.
    , Proceedings of the International Symposium on Biomedical Imaging (ISBI), (2012)
  7. Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images,
    Shan, L., Charles C., and Niethammer M.
    , Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), (2011)
  8. Automatic Bone Segmentation and Alignment from MR knee images,
    Shan, L., Zach C., Styner M., Charles C., and Niethammer M.
    , SPIE Medical Imaging, (2010)
  9. Automatic three-label bone segmentation from knee MR images,
    Shan, L., Zach C., and Niethammer M.
    , International Symposium on Biomedical Imaging (ISBI), (2010)
  10. Globally Optimal Finsler Active Contours,
    Zach, C., Shan L., Frahm J. - M., and Niethammer M.
    , DAGM, p.552–561, (2009)