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.