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Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

概化理论 分割 深度学习 人工智能 公制(单位) 医学物理学 医学 临床实习 头颈部 计算机科学 放射治疗计划 数据集 放射治疗 机器学习 放射科 外科 物理疗法 统计 数学 运营管理 经济
作者
Stanislav Nikolov,Sam Blackwell,Alexei Zverovitch,R. Mendes,Michelle Livne,Jeffrey De Fauw,Yojan Patel,Clemens Meyer,Harry Askham,Bernardino Romera‐Paredes,Christopher Kelly,Alan Karthikesalingam,Carlton Chu,Dawn Carnell,C.S. Boon,D. D’Souza,Syed Moinuddin,Bethany Garie,Yasmin McQuinlan,Sarah Ireland,Kiarna Hampton,Krystle Fuller,Hugh Montgomery,Geraint Rees,Mustafa Suleyman,Trevor Back,Cían Hughes,Joseph R. Ledsam,Olaf Ronneberger
出处
期刊:Journal of Medical Internet Research [JMIR Publications]
卷期号:23 (7): e26151-e26151 被引量:241
标识
DOI:10.2196/26151
摘要

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.
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