RimSet: Quantitatively Identifying and Characterizing Chronic Active
  Multiple Sclerosis Lesion on Quantitative Susceptibility Maps
Hang Zhang Thanh D. Nguyen Jinwei Zhang Renjiu Hu Susan A. Gauthier Yi Wang
Background: Rim+ lesions in multiple sclerosis (MS), detectable via
Quantitative Susceptibility Mapping (QSM), correlate with increased disability.
Existing literature lacks quantitative analysis of these lesions. We introduce
RimSet for quantitative identification and characterization of rim+ lesions on
QSM. Methods: RimSet combines RimSeg, an unsupervised segmentation method using
level-set methodology, and radiomic measurements with Local Binary Pattern
texture descriptors. We validated RimSet using simulated QSM images and an in
vivo dataset of 172 MS subjects with 177 rim+ and 3986 rim-lesions. Results:
RimSeg achieved a 78.7% Dice score against the ground truth, with challenges in
partial rim lesions. RimSet detected rim+ lesions with a partial ROC AUC of
0.808 and PR AUC of 0.737, surpassing existing methods. QSMRim-Net showed the
lowest mean square error (0.85) and high correlation (0.91; 95% CI: 0.88, 0.93)
with expert annotations at the subject level.