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Proceedings Paper

Evaluation of an automatic multiple sclerosis lesion quantification tool in an informatics-based MS e-folder system
Author(s): Kevin Ma; James Fernandez; Lilyana Amezcua; Alex Lerner; Brent Liu
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Paper Abstract

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system. The chronic nature of MS necessitates multiple MRI studies to track disease progression. We have presented an imaging informatics decision-support system, called MS eFolder, designed to integrate patient clinical data with MR images and a computer-aided detection (CAD) component for automatic white matter lesion quantification. The purpose of the MS eFolder is to comprehensively present MS patient data for clinicians and radiologists, while providing a lesion quantification tool that can be objective and consistent for MS tracking in longitudinal studies. The MS CAD algorithm is based on the K-nearest neighbor (KNN) principles and has been integrated within the eFolder system. Currently, the system has been completed and the CAD algorithm for quantifying MS lesions has undergone the expert evaluation in order to validate system performance and accuracy. The evaluation methodology has been developed and the data has been collected, including over 100 MS MRI cases with various age and ethnic backgrounds. The preliminary results of the evaluation are expected to include sensitivity and specificity of lesion and non-lesion voxels in the white matter, the effectiveness of different probability thresholds for each voxel, and comparison between CAD quantification results and radiologists' manual readings. The results aim to show the effectiveness of a MS lesion CAD system to be used in a clinical setting, as well as a step closer to full clinical implementation of the eFolder system.

Paper Details

Date Published: 24 March 2011
PDF: 10 pages
Proc. SPIE 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 79670K (24 March 2011); doi: 10.1117/12.878297
Show Author Affiliations
Kevin Ma, The Univ. of Southern California (United States)
James Fernandez, The Univ. of Southern California (United States)
Lilyana Amezcua, The Univ. of Southern California (United States)
Alex Lerner, The Univ. of Southern California (United States)
Brent Liu, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 7967:
Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications
William W. Boonn; Brent J. Liu, Editor(s)

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