Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs. Methods: We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance. Results: For the 15 test patients, the 3D cascade nnUNet model obtained the highest Dice score of 72.2 +- 22.3 for mediastinal LNs with short axis diameter $\geq$ 8mm and 54.8 +- 23.8 for all LNs respectively. These results represent an improvement of 10 points over a current approach that was evaluated on the same test dataset. Conclusion: To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has immense potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.

翻译：目的：胸部淋巴结因肺癌、肺炎等病理因素易发生肿大。临床医生常通过测量淋巴结尺寸监测疾病进展、确认转移性癌变并评估治疗反应。然而，淋巴结形态与外观的差异性使其识别过程繁琐，且这些结构位于多数器官外部。方法：我们提出利用公共TotalSegmentator工具生成的28个不同结构（如肺、气管等）的解剖先验，对纵隔淋巴结进行分割。采用公共NIH CT淋巴结数据集中89例患者的CT容积图像，训练三个3D nnUNet模型进行淋巴结分割。使用包含15例患者（分布外训练）的公共St. Olavs数据集评估分割性能。结果：针对15例测试患者，3D级联nnUNet模型对短轴直径≥8mm的纵隔淋巴结获得最高Dice系数72.2±22.3，对所有淋巴结获得54.8±23.8。这些结果较当前在相同测试集上评估的方法提升10个百分点。结论：据我们所知，本研究首次利用28个不同解剖先验实现纵隔淋巴结分割，且该方法可拓展至身体其他淋巴结区域。该技术通过识别初始分期CT扫描中的肿大淋巴结，对改善患者预后具有巨大潜力。