Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture
Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture
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DOI：
10.1002/mp.14827
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发表时间：
2021-06-22
期刊：
影响因子：
3.8
通讯作者：
Cardenas, Carlos E.
中科院分区：
文献类型：
--
作者：
Gronberg, Mary P.;Gay, Skylar S.;Cardenas, Carlos E.
目的 放射治疗计划是一个耗时且反复的手动过程。因此，机构之间和机构内部的计划质量差异很大。人工智能在提高计划质量和减少计划时间方面显示出巨大的前景。本技术说明描述了我们参与美国医学物理学家协会开放知识规划挑战赛 (OpenKBP)，这是一项准确预测放射治疗剂量分布的竞赛。方法 开发了具有扩张卷积的三维 (3D) 密集连接 U-Net，以头颈部患者的轮廓 CT 图像作为输入来预测 3D 剂量分布。虽然对旋转和平移等传统增强技术进行了探索，但发现仅对随机补丁进行训练可以产生最佳的模型性能。采用定制加权均方误差损失函数。最后，使用一组性能最佳的网络来生成最终的挑战预测。结果 我们的团队 (SuperPod) 在 OpenKBP 挑战的剂量流中排名第二。测试数据集的预测剂量分布和临床剂量分布之间的平均绝对差异为 2.56 Gy。平均而言，预测的标准化目标 DVH 指标在临床计划的 3% 范围内，预测的危险器官 DVH 指标在临床计划的 2 Gy 范围内。结论 开发的 3D 密集扩张 U-Net 架构可以准确预测 3D 放射治疗剂量分布，并可用作全自动放射治疗计划流程的一部分。
Purpose Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reducing planning times. This technical note describes our participation in the American Association of Physicists in Medicine Open Knowledge-Based Planning Challenge (OpenKBP), a competition to accurately predict radiation therapy dose distributions. Methods A three-dimensional (3D) densely connected U-Net with dilated convolutions was developed to predict 3D dose distributions given contoured CT images of head and neck patients as input. While traditional augmentation techniques such as rotations and translations were explored, it was found that training on random patches alone resulted in the greatest model performance. A custom-weighted mean squared error loss function was employed. Finally, an ensemble of best-performing networks was used to generate the final challenge predictions. Results Our team (SuperPod) placed second in the dose stream of the OpenKBP challenge. The average mean absolute difference between the predicted and clinical dose distributions of the testing dataset was 2.56 Gy. On average, the predicted normalized target DVH metrics were within 3% of the clinical plans, and the predicted organ at risk DVH metrics were within 2 Gy of the clinical plans. Conclusions The developed 3D dense dilated U-Net architecture can accurately predict 3D radiotherapy dose distributions and can be used as part of a fully automated radiation therapy planning pipeline.