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Proceedings Paper

Multi-output decision trees for lesion segmentation in multiple sclerosis
Author(s): Amod Jog; Aaron Carass; Dzung L. Pham; Jerry L. Prince
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Paper Abstract

Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94131C (20 March 2015); doi: 10.1117/12.2082157
Show Author Affiliations
Amod Jog, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Dzung L. Pham, Henry M. Jackson Foundation (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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