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Activity Number: 629
Type: Invited
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #318047 View Presentation
Title: Statistical Segmentation of Multiple Sclerosis Lesions on Structural Magnetic Resonance Images
Author(s): Ciprian Crainiceanu*
Companies: The Johns Hopkins University
Keywords: multiple sclerosis ; lesions ; prediction ; brain imaging ; segmentation

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for evaluating disease-modifying therapies and monitoring disease progression. In practice, lesion load is quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra- observer variability. We propose Subtraction-Based Logistic Inference for Modeling and Estimation (SuBLIME) and OASIS is Automated Statistical Inference for Segmentation (OASIS). SuBLIME is an automated method for segmenting incident lesion voxels between baseline and follow-up MRI studies. OASIS is an automated method for segmenting lesion voxels from a single MRI study. Both methods use intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery (FLAIR) and proton density (PD) MRI volumes. Manual lesion segmentations are used to train and validate logistic regression models. Methods are validated on MRI studies acquired at two separate imaging centers.

Authors who are presenting talks have a * after their name.

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