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.

翻译：脑膜淋巴管负责清除人脑中的代谢废物。其功能损伤与衰老以及多发性硬化症和阿尔茨海默病等脑部疾病相关。然而，脑膜淋巴管直到最近才首次在磁共振成像中被描述，其分支结构使得手动分割尤为困难。此外，由于对其外观缺乏一致的定义，人工标注的脑膜淋巴管结构存在较高的标注者间差异，而大多数自动分割方法无法考虑这一因素。在本工作中，我们为流行的nnU-Net模型提出了一种新的标注者感知训练方案，并探索了基于标注者的集成策略，以实现脑膜淋巴管的准确且一致的分割。这使得我们能够提升nnU-Net的性能，同时获得不同标注风格的显式预测以及基于标注者的不确定性估计。我们的最终模型MLV$^2$-Net相对于人工参考标准达到了0.806的Dice相似系数。该模型进一步匹配了人工标注者间的可靠性，并复现了脑膜淋巴管体积与年龄的相关性。