Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.
Department of Radiation Oncology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
PLoS One. 2024 Jul 31;19(7):e0308181. doi: 10.1371/journal.pone.0308181. eCollection 2024.
Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer.
Two computed tomography (CT) datasets with well-defined labels, including contoured prostate, bladder, and rectum, were obtained from 18 patients. The labels of the CT images captured during radiation therapy (CT2nd) were predicted using CT images scanned before radiation therapy (CT1st). From the deformable vector fields (DVFs) created by using the VoxelMorph method, 10 DVFs were extracted when each of the modified CT and CT2nd images were deformed and registered to the fixed CT1st image. Augmented images were acquired by utilizing 110 extracted DVFs and spatially transforming the CT1st images and labels. An nnU-net autosegmentation network was trained by using the augmented images, and the CT2nd label was predicted. A patient-specific model was created for 18 patients, and the performances of the individual models were evaluated. The results were evaluated by employing the Dice similarity coefficient (DSC), average Hausdorff distance, and mean surface distance. The accuracy of the proposed model was compared with those of models trained with large datasets.
Patient-specific models were developed successfully. For the proposed method, the DSC values of the actual and predicted labels for the bladder, prostate, and rectum were 0.94 ± 0.03, 0.84 ± 0.07, and 0.83 ± 0.04, respectively.
We demonstrated the feasibility of automatic segmentation by employing individual patient datasets and image augmentation techniques. The proposed method has potential for clinical application in automatic prostate segmentation for ART.
在自适应放疗（ART）中，危险器官分割至关重要。基于学习的自动分割可以减少投入的工作量并加速 ART 过程。在这项研究中，我们开发了一种自动分割模型，该模型通过使用个体患者数据集和基于深度学习的增强方法，根据前列腺癌患者靶区和感兴趣器官的变化来定制放疗。
从 18 名患者中获得了两个具有明确标签的计算机断层扫描（CT）数据集，包括勾画的前列腺、膀胱和直肠。使用 VoxelMorph 方法创建的变形向量场（DVF），在对每个修改后的 CT 和 CT2nd 图像进行变形和配准到固定的 CT1st 图像时，提取了 10 个 DVF。通过利用 110 个提取的 DVF 来获取增强图像，并对 CT1st 图像和标签进行空间变换。使用增强图像对 nnU-net 自动分割网络进行训练，并预测 CT2nd 标签。为 18 名患者创建了患者特异性模型，并评估了每个模型的性能。通过使用 Dice 相似系数（DSC）、平均 Hausdorff 距离和平均表面距离来评估结果。还比较了所提出的模型与使用大型数据集训练的模型的准确性。
成功开发了患者特异性模型。对于所提出的方法，膀胱、前列腺和直肠的实际和预测标签的 DSC 值分别为 0.94 ± 0.03、0.84 ± 0.07 和 0.83 ± 0.04。
我们证明了通过使用个体患者数据集和图像增强技术进行自动分割的可行性。该方法有可能在自动前列腺分割用于 ART 中进行临床应用。
