A deep learning-based approach for Multiple Sclerosis Lesion Segmentation

Abstract

Purpose: Multiple Sclerosis (MS) is a chronic disease of the Central Nervous System (CNS), characterized by the presence of disseminated lesions in the brain and Spinal Cord (SC). Magnetic Resonance Imaging (MRI) has become an essential tool for studying the anatomy and functions of the CNS in vivo, enabling not only the identification of brain structures but also the detection of damaged tissue in various neurodegenerative diseases, including MS. The segmentation of lesions on MR images is a crucial step in the diagnosis and monitoring of the disease. However, manual segmentation of MS lesions is a complex and time-consuming task requiring considerable expertise. Methods: This paper proposes a fully automated method for MS lesion segmentation based on a Convolutional Neural Network (CNN) architecture. The model was trained on datasets from the MICCAI 2016 and ISBI 2015 international challenges. FLAIR images from these databases were used as input to the CNN. Results: The results show a significant improvement in the accuracy and robustness of the model, resulting in high-quality segmentation of MS lesions. The model achieved remarkable performance, with a Dice Similarity Coefficient (DSC) of over 89%, outperforming recent methods. Conclusion: These promising results underline the considerable potential for future advances in the automated segmentation of MS lesions.