Difference between revisions of "Resources"

From IACL
Jump to navigation Jump to search
(Changed order.)
(Added link to the updated Challenge Webpage.)
Line 19: Line 19:
  
 
{{h2|Brain}}
 
{{h2|Brain}}
 +
{{h3|2015 Longitudinal MS Lesion Segmentation Challenge}}
 +
* The [[MSChallenge|2015 Longitudinal MS Lesion Segmentation Challenge]] provides training and testing data for segmenting MS lesions over a multiple time-points of 14 patietns. Results can be tested through an automated website.
 +
 +
 
{{h3|Validation Data for Cortical Reconstruction Algorithms}}
 
{{h3|Validation Data for Cortical Reconstruction Algorithms}}
 
* The [[cortical_data/|cortical validation resource]] for evaluation of cortical reconstruction algorithms on both normal subjects and subjects with White Matter lesions.
 
* The [[cortical_data/|cortical validation resource]] for evaluation of cortical reconstruction algorithms on both normal subjects and subjects with White Matter lesions.

Revision as of 23:36, 18 November 2016

<meta name="title" content="Resources"/>

General Image Processing

GVF Software


JIST


MGDM

  • 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.


Brain

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 using Graph Cuts

  • The 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)
  • Cerebellar Lobule Segmentation Code can be used to parcellate the cerebellum into lobules given a T1w MRI image.


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


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.


PET Attentuation Correction


MIMECS


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


Cardiac

HARP

  • 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.


Retinal

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.