Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.
Publication/Presentation Date
3-1-2008
Abstract
RATIONALE AND OBJECTIVES: Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important.
MATERIALS AND METHODS: In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans.
RESULTS: Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set.
CONCLUSIONS: Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.
Volume
15
Issue
3
First Page
300
Last Page
313
ISSN
1076-6332
Published In/Presented At
Lao, Z., Shen, D., Liu, D., Jawad, A. F., Melhem, E. R., Launer, L. J., Bryan, R. N., & Davatzikos, C. (2008). Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Academic radiology, 15(3), 300–313. https://doi.org/10.1016/j.acra.2007.10.012
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
18280928
Department(s)
Administration and Leadership
Document Type
Article