新工具可自动识别多发性硬化症的磁性环状病灶并分割边缘，提升诊断效率。
QSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis
- 基于U-Net模型，仅用常规MRI图像实现病灶检测与边缘分割。
- 分割准确率中位数达0.57，检测性能比前代方法提升46.7%。
- 适合神经影像研究者和临床医生用于快速分析多发性硬化病灶。
磁性环状病灶（PRLs）是多发性硬化症的新兴生物标志物。手动在定量磁敏感性成像（QSM）上识别和分割PRLs耗时费力。现有深度学习方法QSM-RimNet可自动检测PRLs，但无法提供边缘分割且需精确的QSM病灶掩码。本研究提出基于U-Net的QSM-RimDS方法，仅需常规T2加权液体抑制反转恢复（FLAIR）病灶掩码，即可实现联合检测与边缘分割。两名专家作为参考标准进行病灶分类与边缘标注。采用骰子相似系数（DSC）评估分割一致性。在五折交叉验证中，比较了QSM-RimDS与QSM-RimNet的检测性能。共识别出260个PRLs（3.3%）和7720个非PRLs（96.7%）。相比专家标注，QSM-RimDS平均DSC为0.57±0.02，5次验证中73.8%的病灶达到中高一致（DSC≥0.5）。QSM-RimDS在精确率-召回率曲线下平均面积（AUC）达0.754±0.037，较QSM-RimNet的0.514±0.121提升46.7%；受试者工作特征曲线下面积（AUC）为0.956±0.034，优于0.908±0.073。结论：相较于QSM-RimNet，QSM-RimDS显著提升检测精度，且可提供合理准确的边缘分割。
原文摘要 · Abstract (English)
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.
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