Over half a million individuals are diagnosed with head and neck cancer each
year worldwide. Radiotherapy is an important curative treatment for this
disease, but it requires manual time consuming delineation of radio-sensitive
organs at risk (OARs). This planning process can delay treatment, while also
introducing inter-operator variability with resulting downstream radiation dose
differences. While auto-segmentation algorithms offer a potentially time-saving
solution, the challenges in defining, quantifying and achieving expert
performance remain. Adopting a deep learning approach, we demonstrate a 3D
U-Net architecture that achieves expert-level performance in delineating 21
distinct head and neck OARs commonly segmented in clinical practice. The model
was trained on a dataset of 663 deidentified computed tomography (CT) scans
acquired in routine clinical practice and with both segmentations taken from
clinical practice and segmentations created by experienced radiographers as
part of this research, all in accordance with consensus OAR definitions. We
demonstrate the model's clinical applicability by assessing its performance on
a test set of 21 CT scans from clinical practice, each with the 21 OARs
segmented by two independent experts. We also introduce surface Dice similarity
coefficient (surface DSC), a new metric for the comparison of organ
delineation, to quantify deviation between OAR surface contours rather than
volumes, better reflecting the clinical task of correcting errors in the
automated organ segmentations. The model's generalisability is then
demonstrated on two distinct open source datasets, reflecting different centres
and countries to model training. With appropriate validation studies and
regulatory approvals, this system could improve the efficiency, consistency,
and safety of radiotherapy pathways.

采用深度学习方法，训练了一种 3D U-Net 架构的自动分割算法，成功实现了 21 个头颈部放射敏感器官的自动分割，同时引入了表面 Dice 相似系数（surface DSC）一种能更好地反映纠正自动分割错误的临床任务的新指标，表明该模型在不同研究中心和国家具有较强的泛化能力，并可通过适当的验证研究和监管审批实现提高放射治疗路径的效率、一致性和安全性的目标。