Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networks

Automatic segmentation of Multiple Sclerosis (MS) lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. A reliable, automatic segmentation method can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this paper, we present a fully automated method for MS lesion segmentation. The proposed method uses MR intensities and White Matter (WM) priors for extraction of candidate lesion voxels and uses Convolutional Neural Networks for false positive reduction. Our networks process longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-the-art Dice results with the performance level of a trained human rater.