Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI

In this paper, we propose an automated segmentation approach based on a deep two-dimensional fully convolutional neural network to segment brain multiple sclerosis lesions from multimodal magnetic resonance images. The proposed model is made as a combination of two deep subnetworks. An encoding network extracts different feature maps at various resolutions. A decoding part upconvolves the feature maps combining them through shortcut connections during an upsampling procedure. To the best of our knowledge, the proposed model is the first slice-based fully convolutional neural network for the purpose of multiple sclerosis lesion segmentation. We evaluated our network on a freely available dataset from ISBI MS challenge with encouraging results from a clinical perspective.