A multi-sequence MRI integration framework using SwinUNETR-v2 for multiple sclerosis lesion segmentation

Abstract

Multiple Sclerosis (MS) is a chronic brain disease that affects the brain and spinal cord, where Magnetic Resonance Imaging (MRI) plays a key role in diagnosis. While manual analysis of brain MRIs is important, it is time-consuming and prone to human error. Artificial Intelligence (AI)-driven Computer Aided Diagnostic (CAD) systems have therefore gained traction due to their ability to provide more consistent and reliable assessments. This study presents a multi-sequence framework that integrates four MRI modalities with the SwinUNETR-v2 backbone for MS lesion segmentation. The main contribution is a task-oriented integration of multi-sequence input design (implemented as a four-channel volume representation), refined preprocessing (including balanced foreground/background patch extraction), and a weighted loss formulation under a controlled five-fold evaluation protocol. This approach achieved a peak DSC of 90.7% and a mean DSC of 88.3%. Moreover, when compared against other state-of-the-art segmentation methods—including AttentionUNet, DenseResidualUNet, SegResNet, FCNN, and nnUNet-v2—the multi-sequence SwinUNETR-v2 setup consistently outperformed these models across all key metrics, demonstrating strong effectiveness in identifying MS lesions.