Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.
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DOI：
10.2196/26151
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发表时间：
2021-07-12
影响因子：
7.4
通讯作者：
Ronneberger O
中科院分区：
文献类型：
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作者：
Nikolov S;Blackwell S;Zverovitch A;Mendes R;Livne M;De Fauw J;Patel Y;Meyer C;Askham H;Romera-Paredes B;Kelly C;Karthikesalingam A;Chu C;Carnell D;Boon C;D'Souza D;Moinuddin SA;Garie B;McQuinlan Y;Ireland S;Hampton K;Fuller K;Montgomery H;Rees G;Suleyman M;Back T;Hughes CO;Ledsam JR;Ronneberger O
全球每年有超过50万人被诊断患有头颈癌。放射治疗是一种重要的治疗方法，但它需要手动时间来描绘放射敏感器官的风险。这种计划过程可能会延迟治疗，同时也会引入手术操作者的可变性，导致下游辐射剂量差异。尽管自动分割算法提供了一种可能节省时间的解决方案，但在定义、量化和实现专家性能方面的挑战仍然存在。采用深度学习方法，我们的目标是展示一个3D U-Net架构，该架构在描绘临床实践中通常分割的21个不同的头颈部危险器官方面达到专家级性能。该模型是在常规临床实践中获得的663个去识别计算机断层扫描的数据集上训练的，并且具有来自临床实践的分割和由经验丰富的放射技师创建的分割，作为本研究的一部分，所有这些都符合共识的风险器官定义。我们证明了该模型的临床适用性，通过评估其性能的测试集的21个计算机断层扫描从临床实践中，每个有21个器官的风险由2个独立的专家分割。我们还引入了表面Dice相似性系数，这是一种用于比较器官描绘的新指标，用于量化危险器官表面轮廓而不是体积之间的偏差，更好地反映了自动器官分割中纠正错误的临床任务。然后，在2个不同的开源数据集上证明了模型的通用性，反映了不同的中心和国家的模型训练。深度学习是一种有效且临床适用的技术，用于分割头颈部解剖结构以进行放射治疗。通过适当的验证研究和监管批准，该系统可以提高放射治疗路径的效率，一致性和安全性。
Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although 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 aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. The model was trained on a data set of 663 deidentified computed tomography 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 organ at risk definitions. We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.