QSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis
Journal:
arXiv
Published Date:
Dec 13, 2024
Abstract
Paramagnetic rim lesions (PRLs) are an emerging biomarker in multiple
sclerosis (MS). Manual identification and rim segmentation of PRLs on
quantitative susceptibility mapping (QSM) images are time-consuming. Deep
learning-based QSM-RimNet can provide automated PRL detection, but this method
does not provide rim segmentation for microglial density quantification and
requires precise QSM lesion masks. The purpose of this study is to develop a
U-Net-based QSM-RimDS method for joint PRL detection and rim segmentation using
readily available T2-weighted (T2W) fluid-attenuated inversion recovery (FLAIR)
lesion masks. Two expert readers performed PRL classification and rim
segmentation as the reference. Dice similarity coefficient (DSC) was used to
assess the agreement between rim segmentation obtained by QSM-RimDS and the
manual expert segmentation. The PRL detection performances of QSM-RimDS and
QSM-RimNet were evaluated using receiver operating characteristic (ROC) and
precision-recall (PR) plots in a five-fold cross validation. A total of 260
PRLs (3.3\%) and 7720 non-PRLs (96.7\%) were identified by the readers.
Compared to the expert rim segmentation, QSM-RimDS provided a mean DSC of 0.57
\pm 0.02 with moderate to high agreement (DSC \leq 0.5) in 73.8pm 5.7\% of PRLs
over five folds. QSM-RimDS produced better and more consistent detection
performance with a mean area under curve (AUC) of 0.754 \pm 0.037 vs. 0.514 \pm
0.121 by QSM-RimNet (46.7\% improvement) on PR plots, and 0.956 \pm 0.034 vs.
0.908 \pm 0.073 (5.3\% improvement) on ROC plots. In conclusion, QSM-RimDS
improves PRL detection accuracy compared to QSM-RimNet and unlike QSM-RimNet
can provide reasonably accurate rim segmentation.
