The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep-learning methods depend heavily on consistent training data; thus, State-of-the-Art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on the surrounding anatomical structures. Training strategies that directly follow the construction rules given in the expert guidelines or the possibility of quantifying the conformance of manually drawn contours to the guidelines are still missing. Seventy-one anatomical structures that are relevant to CTV delineation in head- and neck-cancer patients, according to the expert guidelines, were segmented on 104 computed tomography scans, to assess the possibility of automating their segmentation by State-of-the-Art deep learning methods. All 71 anatomical structures were subdivided into three subsets of non-overlapping structures, and a 3D nnU-Net model with five-fold cross-validation was trained for each subset, to automatically segment the structures on planning computed tomography scans. We report the DICE, Hausdorff distance and surface DICE for 71 + 5 anatomical structures, for most of which no previous segmentation accuracies have been reported. For those structures for which prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2 mm margin was larger than 80% for almost all the structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.
放射治疗临床靶区（CTV）的勾画过程耗时、需要高强度培训，且存在显著的观察者间差异。监督式深度学习方法高度依赖一致的训练数据，因此前沿研究致力于提升CTV标签的一致性并严格遵循现行标准。国际共识专家指南通过将临床靶区扩展范围与周围解剖结构关联，实现了CTV勾画的标准化。然而，目前仍缺乏直接遵循专家指南构建规则的训练策略，以及量化人工勾画轮廓与指南符合程度的方法。本研究依据专家指南，在104例头颈癌患者的计算机断层扫描图像中，分割出71个与CTV勾画相关的解剖结构，以评估采用前沿深度学习方法实现自动分割的可行性。将全部71个解剖结构划分为三个互不重叠的子集，采用五折交叉验证分别训练三维nnU-Net模型，实现计划CT扫描中解剖结构的自动分割。我们报告了71+5个解剖结构的DICE系数、豪斯多夫距离和表面DICE值，其中多数结构的分割精度属首次报道。对于已有文献报道的结构，本研究的分割精度达到或超越了现有水平。所有模型预测结果均优于TotalSegmentator的预测性能。在2毫米容差范围内，几乎所有结构的表面DICE值均超过80%。针对分割精度降低的个别结构，我们结合专家指南分析了其对CTV勾画的影响。分析表明，这些偏差预计不会影响基于规则的CTV自动勾画流程。
肿瘤(癌症)患者之家