Lesion Instance Segmentation in Multiple Sclerosis: Assessing the Efficacy of Statistical Lesion Splitting

Motivation: Accurate white matter lesion (WML) counting and delineation are crucial for multiple sclerosis (MS) diagnosis and prognosis. Though being a critical step in clinical research and automated tools relying on lesion-centered patches, no previous work studied post-processing methods to transform voxel-wise segmentations into lesion instance masks in MS. Goal(s): In this study, we compare the conventional connected components (CC) method to a confluent lesion splitting (CLS) method that was used but never validated. Approach: CC and CLS's performances are evaluated using three common lesion segmentation tools (LSTs): SPM, SAMSEG, and nnU-Net. Results: CLS lacks generalization, sacrifices specificity for sensitivity and worsens segmentation quality. Impact: Our results underscore the need for the development of a novel instance segmentation methodology that accounts for (i) the potential large distance between voxels and the center of the lesions to which they belong and (ii) confluent lesions.