Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks

TL;DR

This paper introduces a fully convolutional neural network for segmenting white matter lesions in MS from multi-contrast MRI, demonstrating superior accuracy over existing methods on multiple datasets.

Contribution

The paper presents a novel CNN architecture with dual pathways for multi-contrast MRI lesion segmentation, achieving state-of-the-art results.

Findings

Significant improvement in segmentation quality over existing methods.

Achieved a Dice score of 90.48 on ISBI 2015 challenge data.

Outperformed four publicly available MS lesion segmentation methods.

Abstract

Multiple Sclerosis (MS) is an autoimmune disease that leads to lesions in the central nervous system. Magnetic resonance (MR) images provide sufficient imaging contrast to visualize and detect lesions, particularly those in the white matter. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Therefore robust and accurate segmentation of white matter lesions from MR images can provide important information about the disease status and progression. In this paper, we propose a fully convolutional neural network (CNN) based method to segment white matter lesions from multi-contrast MR images. The proposed CNN based method contains two convolutional pathways. The first pathway consists of multiple parallel convolutional filter banks catering to multiple MR modalities. In the second pathway, the outputs of the first one are concatenated and another set of convolutional filters are applied. The output of this last pathway produces a membership function for lesions that may be thresholded to obtain a binary segmentation. The proposed method is evaluated on a dataset of 100 MS patients, as well as the ISBI 2015 challenge data consisting of 14 patients. The comparison is performed against four publicly available MS lesion segmentation methods. Significant improvement in segmentation quality over the competing methods is demonstrated on various metrics, such as Dice and false positive ratio. While evaluating on the ISBI 2015 challenge data, our method produces a score of 90.48, where a score of 90 is considered to be comparable to a human rater.