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A Light Weighted Deep Learning Framework for Multiple Sclerosis Lesion Segmentation

This paper presents a fully automated light weighted convolutional network for multiple sclerosis(MS) lesion segmentation from multimodal magnetic resonance(MR) scans which reduces the complexity of U-Net architecture and training time with significant extent. In multiple sclerosis problem, the discrepancy in texture, shape, form, size and location in MR images causes challenges in automatic subdivision of lesions. Other challenge we face, when we train these networks with unbalanced data. The MR images constitute unbalanced data because they contain more number of non-lesion class voxels than the lesion class voxels. If a network is trained with unbalanced data, the prediction with higher precision and lower recall can be made with a bias approaching the non-lesion class. This is undesirable in most of the clinical diagnostic purposes because false negatives are more serious than false positives. The proposed network has concentrated to solve these challenges to a desirable extent. The outputs of the network confirm that the recommended architecture is able to obtain higher accuracy and dice similarity co-efficient(DSC) as compared to some of the state-of-the-art methods. The performance of the networks is estimated by using randomized 5-fold subject-independent cross validation which helps us to judge the robustness and efficacy of the networks in MS lesion segmentation.