  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.
