Keywords: Analysis/Processing, Segmentation, Instance Segmentation
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords