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General Image Processing

GVF Software



  • Source code and demonstrations for the Multiple-object Geometric Deformable Model (MGDM) can be found on the MGDM project page hosted by NITRC.
  • A movie demo of the decomposition and evolution of MGDM.


2015 Longitudinal MS Lesion Segmentation Challenge

Validation Data for Cortical Reconstruction Algorithms

  • The cortical validation resource for evaluation of cortical reconstruction algorithms on both normal subjects and subjects with White Matter lesions.

Cerebellar Lobule Segmentation

  • Our graph-cut based segmentation of the cerebellum described in Z. Yang, C. Ye, J.A. Bogovic, A. Carass, B.M. Jedynak, S.H. Ying, and J.L. Prince, "Automated Cerebellar Lobule Segmentation with Application to Cerebellar Structural Analysis in Cerebellar Disease", NeuroImage, 127:435-444, 2016. (doi)

Temporal Filtering for Consistent Segmentation

  • The temporal filtering of longitudinal MR images of the brain described in S. Roy, A. Carass, J. Pacheco, M. Bilgel, S.M. Resnick, J.L. Prince, and D.L. Pham, "Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation", NeuroImage: Clinical, 11:264-275, 2016. (doi)
  • Matlab executables are available

PET Attentuation Correction

Rician Mixture Model

  • The Rician mixture model for segmenting the brain is described in S. Roy, A. Carass, P.-L. Bazin, S.M. Resnick, and J.L. Prince, "Consistent Segmentation using a Rician Classifier", Medical Image Analysis, 16(2):524-535, 2012. (PDF) (doi) (PubMed)
  • Matlab executables are available.

Progression Score Model

  • The Progression Score Model Toolkit, including the expectation-maximization (EM) algorithm for fitting the nonlinear mixed effects model, can be obtained from the NITRC project page.

IMAGE Synthesis


Subject Specific Dictionary Learning (S3DL)

  • S3DL described in S. Roy, A. Carass, J.L. Prince, and D.L. Pham, "Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation", Fifth International Workshop on Machine Learning in Medical Imaging (MLMI 2014), Boston, MA, September 14, 2014. (doi) and S. Roy, Q. He, E. Sweeney, A. Carass, D.S. Reich, J.L. Prince, and D.L. Pham, "Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation", IEEE Journal of Biomedical and Health Informatics, 19(5):1598-1609, 2015. (doi)
  • Matlab executables are available.


  • Regression Ensembles with Patch Learning for Image Contrast Agreement (REPLICA) described in A. Jog, A. Carass, S. Roy, D.L. Pham, and J.L. Prince, "Random Forest Regression for Magnetic Resonance Image Synthesis", Medical Image Analysis, 35:475-488, 2017. (doi) (PubMed)
  • Code and sample data is available on our NITRC Project Page titled Image Synthesis.



  • For MATLAB demonstration software send an email to Harp email.jpg and expect a reply within five business days. We also have a collection of frequently asked questions about our HARP software.


AURA Tools

  • The AURA Tools software package allows for the automated processing and segmentation of Optical Coherence Tomography images of the macula cube. It is available from the NITRC website.


  • The Automated segmentation of mouse OCT volumes (ASiMOV) toolkit is now available from the AURA Tools NITRC page.
  • Version 0.1 includes the software originally presented in: B.J. Antony, B.-J. Kim, A. Lang, A. Carass, J.L. Prince, and D.J. Zack, "Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model", PLoS ONE, 12(8);e0181059, 2017. (doi) (PubMed)
  • Direct download link for ASiMOV v0.1

OCT MS and Healthy Controls Data

  • Data resource for Multiple Sclerosis and Healthy Controls:
  • (1.8G) contains 35 OCT volumes from a Spectralis Scanner with corresponding manual delineations of nine retinal boundaries. The cohort contains 14 healthy controls and 21 multiple sclerosis patients with age and gender information.
  • The preprocessing and data reader code are part of AURA Tools.

IACL Mouse Data

  • In conjunction with the paper: B. J. Antony, B.-J. Kim, A. Lang, A. Carass, J. L. Prince, and D. J. Zack, “Automated Segmentation of Mouse OCT Volumes (ASiMOV): Validation & Clinical Study of a Light Damage Model”, PLOS ONE, In Press. We release which is the data and results presented in this paper. When using this data please cite the PLOS ONE paper. The download is 15.2GB.