[论文解读] Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Stanislav Nikolov, Sam Blackwell+27 more|arXiv (Cornell University)|Sep 12, 2018

Advanced Radiotherapy Techniques参考文献 85被引用 248

一句话总结

基于3D U-Net的模型在CT计划扫描中以专家放射技师水平分割21个头颈部风险器官（OAR），并引入一种新的表面Dice相似系数以反映临床修正工作量，在多个数据集上展示了泛化能力。

ABSTRACT

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.

研究动机与目标

• 解决放射治疗计划中手工划定头颈部风险器官在时间、变异性和安全性方面的挑战。
• 开发并验证一个深度学习分割模型，在临床代表性数据上达到专家放射技师的表现。
• 提出一个具有临床意义的评估指标（表面Dice相似系数），以反映纠正自动分割所需的努力。

提出的方法

• 采用3D U-Net架构在计划CT扫描上轮廓绘制21个风险器官。
• 在来自日常实践的663例去标识化CT扫描上进行训练，使用临床来源的和放射技师创建的分割。
• 在同一医院的21例CT扫描的测试集上，与两名独立专家进行评估，并增加开源数据集以测试泛化能力。
• 引入表面Dice相似系数（surface DSC），在器官特定容忍度内测量表面重叠，聚焦于可编辑的边界区域而非体积。
• 将模型性能与有经验的放射技师以及肿瘤科医生的真值裁定进行比较，以确立专家水平的性能。

实验结果

研究问题

• RQ1 是否有深度学习模型能够在计划CT扫描中达到对21个头颈部OAR的专家级轮廓绘制？
• RQ2 该模型是否能够泛化到具有不同人群统计和成像协议的外部数据集？
• RQ3 以表面为焦点的评估指标（表面DSC）是否比体积指标更能反映临床修正工作量？
• RQ4 在具有代表性的测试集上，模型相对于放射技师和肿瘤科医生的性能比较如何？

主要发现

• 该模型在UCLH测试集中，对所有21个OAR在器官特定容忍度内的表面DSC表现与放射技师相似。
• 在TCIA公开测试集上，模型在19/21个OAR上与放射技师相匹配，两个器官（脑干和右晶状体）的表现低于放射技师，可能是因为图像质量原因。
• 引入了一种新颖的表面Dice相似系数（surface DSC），用于在器官特定容忍度内量化表面级别的一致性，显示对修正的临床相关评估。
• 在三个测试队列（UCLH、TCIA、PDDCA）中展示了泛化能力，表明对不同中心、人群统计以及扫描仪/协议变异具有鲁棒性。
• 作者发布了他们的TCIA标注数据集，以支持客观比较和未来在放疗计划自动分割方面的研究。