Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
J Digit Imaging. 2023 Jun;36(3):923-931. doi: 10.1007/s10278-023-00779-z. Epub 2023 Jan 30.
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.
本研究旨在评估一种基于深度无监督学习模型的区域变形模型，用于在乳腺锥形束 CT 引导自适应放疗中自动进行轮廓传播。引入了一种深度无监督学习模型，用于将乳房肿瘤床、临床靶区、心脏、左肺、右肺和脊髓从规划 CT 映射到锥形束 CT。为了提高传统图像配准方法的性能，我们使用了基于窄带映射的区域变形框架，该框架可以减轻锥形束 CT 上图像伪影的影响。我们回顾性地从 111 名乳腺癌患者中选择了 373 个匿名锥形束 CT 容积。锥形束 CT 分为三组。311/20/42 个锥形束 CT 图像用于训练、验证和测试。手动轮廓用于测试集的参考。我们比较了参考和模型预测之间的结果，以评估性能。手动参考分割和模型预测分割之间的平均 Dice 系数为乳腺肿瘤床、临床靶区、心脏、左肺、右肺和脊髓分别为 0.78±0.09、0.90±0.03、0.88±0.04、0.94±0.03、0.95±0.02 和 0.77±0.07。结果表明参考和提出的轮廓之间具有良好的一致性。基于深度学习的区域变形模型技术可自动传播乳腺癌自适应放疗的轮廓。在轮廓传播中应用深度学习是有前途的，但需要进一步研究。
