S3DL Sparse Dictionary Learning based MR Image and Lesion Segmentation

S3DL (Subject Specific Sparse Dictionary Learning) is a software tool to generate whole brain segmentation as well as lesion segmentation from multi-contrast human brain MR images. It is a patch-based method, where similarities between patches from a subject and one or more atlases are exploited to create a segmentation of the subject.

There are two parts of the tool, one to create multi-class segmentation from T1-w MR images, another to segment MS lesions from T1-w and FLAIR images. They can be used independently. In addition to the software, some training atlases and test data are provided.

The toolbox is based on the following paper,
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.
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Specifications

Category:Volumetric Analysis
License:Attribution Non-Commercial
Domain:MR
Programming Language:MATLAB
Supported Data Format:NIfTI-1

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MLMI paper posted by Snehashis Roy on Mar 7

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JBHI Paper posted by Snehashis Roy on Mar 7

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s3dl: S3DL Brain Tissue Segmentation 1.0 release

S3DL_nolesion.zip posted by Snehashis Roy on Mar 7

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s3dl: S3DL Lesion Segmentation 1.0 release

S3DL_Lesion_Segmentation.zip posted by Snehashis Roy on Mar 6

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