Documentation/4.1/Extensions/LesionSegmentation

Introduction and Acknowledgements

Extension: LesionSegmentation Acknowledgments: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Author: Mark Scully () Contact: Mark Scully, <email>mark@biomedicalmining.com</email>

National Alliance for Medical Image Computing (NA-MIC)

Module Description

This extension contains multiple CLI modules for segmenting white matter lesions in sMRI data. In order to use these tools your data must include a T1, T2, FLAIR, and brain mask for each subject. All data must be preprocessed including intra-subject co-registration, AC-PC alignment, bias correction, consistent spacing between sequences, and brain mask creation. If you are training a new model then expert lesion segmentations (co-registered to the anatomical scans for the relevant subject) are also required.

Use Cases

1. Training a new model.

1. Segmenting Lesions based on an existing model.

Tutorials

Coming soon!

References

• Scully M, Anderson B, Lane T, Gasparovic C, Magnotta V, Sibbitt W, Roldan C, Kikinis R and Bockholt HJ (2010) An automated method for segmenting white matter lesions through multi-level morphometric feature classification with application to lupus. Front. Hum. Neurosci. doi:10.3389/fnhum.2010.00027

http://frontiersin.org/neuroscience/humanneuroscience/paper/10.3389/fnhum.2010.00027/