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Proceedings Paper

Large deep neural networks for MS lesion segmentation
Author(s): Juan C. Prieto; Michele Cavallari; Miklos Palotai; Alfredo Morales Pinzon; Svetlana Egorova; Martin Styner; Charles R. G. Guttmann
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Paper Abstract

Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330F (24 February 2017); doi: 10.1117/12.2254778
Show Author Affiliations
Juan C. Prieto, The Univ. of North Carolina at Chapel Hill (United States)
Michele Cavallari, Brigham and Women's Hospital (United States)
Miklos Palotai, Brigham and Women's Hospital (United States)
Alfredo Morales Pinzon, Brigham and Women's Hospital (United States)
Svetlana Egorova, Brigham and Women's Hospital (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)
Charles R. G. Guttmann, Brigham and Women's Hospital (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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