针对脑膜淋巴管分割中人工标注差异大的难题，提出基于评分者风格的融合方法。
MLV$^2$-Net: Rater-Based Majority-Label Voting for Consistent Meningeal Lymphatic Vessel Segmentation
- 引入评分者感知训练与基于评分者的集成策略
- Dice系数达0.806，匹配人工标注一致性
- 可识别不同标注风格并提供不确定性估计
脑膜淋巴管（MLVs）负责清除大脑代谢废物，其功能障碍与衰老及多发性硬化、阿尔茨海默病等疾病相关。由于MLVs首次在磁共振成像（MRI）中被描述，且结构复杂，手动分割困难；同时缺乏统一外观标准，导致人工标注存在较高评分者间差异，现有自动分割方法难以应对。本文提出一种面向nnU-Net的评分者感知训练方案，并探索基于评分者的集成策略，实现准确且一致的MLV分割。所提模型MLV$^2$-Net在人类参考标准下获得0.806的Dice相似系数，达到人工标注间的可靠性水平，并复现了年龄与MLV体积之间的关联。
原文摘要 · Abstract (English)
Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease. However, MLVs have only recently been described for the first time in magnetic resonance imaging (MRI), and their ramified structure renders manual segmentation particularly difficult. Further, as there is no consistent notion of their appearance, human-annotated MLV structures contain a high inter-rater variability that most automatic segmentation methods cannot take into account. In this work, we propose a new rater-aware training scheme for the popular nnU-Net model, and we explore rater-based ensembling strategies for accurate and consistent segmentation of MLVs. This enables us to boost nnU-Net's performance while obtaining explicit predictions in different annotation styles and a rater-based uncertainty estimation. Our final model, MLV$^2$-Net, achieves a Dice similarity coefficient of 0.806 with respect to the human reference standard. The model further matches the human inter-rater reliability and replicates age-related associations with MLV volume.
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