Multi-view longitudinal CNN for multiple sclerosis lesion segmentation

In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. A convolutional neural network based method for multiple sclerosis lesion segmentation is proposed.The network utilizes longitudinal data, a novel contribution in the domain of MS lesion analysis.The use of longitudinal data significantly improves segmentation accuracy.State-of-the-art results are obtained on a public benchmark dataset.Expert human level segmentation accuracy is obtained by the proposed method.