F.sh: A 3D Recurrent Residual Attention U-Net for Automated Multiple Sclerosis Lesion Segmentation

Abstract

Listen

Translate article

Multiple sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterized by lesions in the brain and spinal cord. Accurate detection and localization of these lesions on MRI scans is crucial for diagnosis and monitoring disease progression. Manual segmentation is time-consuming and prone to inter-rater variability. This study proposes F.sh (3DR2AUNet), a novel deep learning architecture for automated MS lesion segmentation. F.sh combines 3D recurrent residual blocks, attention gates, and the U-Net structure to effectively capture lesion features. The model was trained and evaluated using a comprehensive approach, including patch-based preprocessing, data augmentation, and a composite loss function combining Binary Cross-Entropy and 3D Dice Loss. Experimental results demonstrate the superior performance of F.sh compared to baseline methods, achieving a Dice score of 0.92. The proposed approach has the potential to assist radiologists in the accurate and efficient assessment of MS lesion burden.