Evaluation of an automatic multiple sclerosis lesion quantification tool in an informatics-based MS e-folder system

Kevin Ma; James Fernandez; Lilyana Amezcua; Alex Lerner; Brent Liu

doi:10.1117/12.878297

24 March 2011 Evaluation of an automatic multiple sclerosis lesion quantification tool in an informatics-based MS e-folder system

Kevin Ma, James Fernandez, Lilyana Amezcua, Alex Lerner, Brent Liu

Author Affiliations +

Proceedings Volume 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications; 79670K (2011) https://doi.org/10.1117/12.878297
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

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Kevin Ma, James Fernandez, Lilyana Amezcua, Alex Lerner, and Brent Liu "Evaluation of an automatic multiple sclerosis lesion quantification tool in an informatics-based MS e-folder system", Proc. SPIE 7967, Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 79670K (24 March 2011); https://doi.org/10.1117/12.878297

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KEYWORDS

Computer aided diagnosis and therapy

Computer aided design

Magnetic resonance imaging

Brain

Image segmentation

Neuroimaging

3D image processing

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