MS lesion segmentation using a multi-channel patch-based approach with spatial consistency

Roey Mechrez; Jacob Goldberger; Hayit Greenspan

doi:10.1117/12.2082558

20 March 2015 MS lesion segmentation using a multi-channel patch-based approach with spatial consistency

Roey Mechrez, Jacob Goldberger, Hayit Greenspan

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94130O (2015) https://doi.org/10.1117/12.2082558
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAIR). An MS lesion patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally a novel iterative patch-based label refinement process based on the initial segmentation map is performed to ensure spatial consistency of the detected lesions. A leave-one-out evaluation is done for each testing image in the MS lesion segmentation challenge of MICCAI 2008. Results are shown to compete with the state-of-the-art methods on the MICCAI 2008 challenge.

Citation Download Citation

Roey Mechrez, Jacob Goldberger, and Hayit Greenspan "MS lesion segmentation using a multi-channel patch-based approach with spatial consistency", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130O (20 March 2015); https://doi.org/10.1117/12.2082558

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