The accurate segmentation of multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) is essential for diagnosis, disease monitoring, and therapeutic assessment. Despite the significant advances in deep learning-based segmentation, the current boundary-aware approaches are limited by their reliance on spatial distance transforms, which fail to fully exploit the rich texture and intensity information inherent in MRI data. This limitation is particularly problematic in regions where MS lesions and normal-appearing white matter exhibit overlapping intensity distributions, resulting in ambiguous boundaries and reduced segmentation accuracy. To address these challenges, we propose a novel Mahalanobis distance map (MDM) and a corresponding Mahalanobis distance loss, which generalize traditional distance transforms by incorporating spatial coordinates, the FLAIR intensity, and radiomic texture features into a unified feature space. Our method leverages the covariance structure of these features to better distinguish ambiguous regions near lesion boundaries, mimicking the texture-aware reasoning of expert radiologists. Experimental evaluation on the ISBI-MS and MSSEG datasets demonstrates that our approach achieves superior performance in both boundary quality metrics (HD95, ASSD) and overall segmentation accuracy (Dice score, precision) compared to state-of-the-art methods. These results highlight the potential of texture-integrated distance metrics to overcome MS lesion segmentation difficulties, providing more reliable and reproducible assessments for MS management and research.
Ulloa-Poblete et al. (Wed,) studied this question.