Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan.
Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan.
J Appl Clin Med Phys. 2023 May;24(5):e13912. doi: 10.1002/acm2.13912. Epub 2023 Jan 19.
The aim of this study was to evaluate generalization ability of segmentation accuracy for limited FOV CBCT in the male pelvic region using a full-image CNN. Auto-segmentation accuracy was evaluated using various datasets with different intensity distributions and FOV sizes.
A total of 171 CBCT datasets from patients with prostate cancer were enrolled. There were 151, 10, and 10 CBCT datasets acquired from Vero4DRT, TrueBeam STx, and Clinac-iX, respectively. The FOV for Vero4DRT, TrueBeam STx, and Clinac-iX was 20, 26, and 25 cm, respectively. The ROIs, including the bladder, prostate, rectum, and seminal vesicles, were manually delineated. The U -Net CNN network architecture was used to train the segmentation model. A total of 131 limited FOV CBCT datasets from Vero4DRT were used for training (104 datasets) and validation (27 datasets); thereafter the rest were for testing. The training routine was set to save the best weight values when the DSC in the validation set was maximized. Segmentation accuracy was qualitatively and quantitatively evaluated between the ground truth and predicted ROIs in the different testing datasets.
The mean scores ± standard deviation of visual evaluation for bladder, prostate, rectum, and seminal vesicle in all treatment machines were 1.0 ± 0.7, 1.5 ± 0.6, 1.4 ± 0.6, and 2.1 ± 0.8 points, respectively. The median DSC values for all imaging devices were ≥0.94 for the bladder, 0.84-0.87 for the prostate and rectum, and 0.48-0.69 for the seminal vesicles. Although the DSC values for the bladder and seminal vesicles were significantly different among the three imaging devices, the DSC value of the bladder changed by less than 1% point. The median MSD values for all imaging devices were ≤1.2 mm for the bladder and 1.4-2.2 mm for the prostate, rectum, and seminal vesicles. The MSD values for the seminal vesicles were significantly different between the three imaging devices.
The proposed method is effective for testing datasets with different intensity distributions and FOV from training datasets.
本研究旨在评估基于全图像卷积神经网络（CNN）的有限视野锥形束 CT（CBCT）在男性骨盆区域的分割准确性的泛化能力。使用具有不同强度分布和视野大小的各种数据集来评估自动分割准确性。
共纳入 171 例前列腺癌患者的 CBCT 数据集。其中，分别有 151、10 和 10 例 CBCT 数据集来自 Vero4DRT、TrueBeam STx 和 Clinac-iX。Vero4DRT、TrueBeam STx 和 Clinac-iX 的视野分别为 20、26 和 25cm。手动勾画膀胱、前列腺、直肠和精囊的 ROI。使用 U-Net CNN 网络架构训练分割模型。总共使用 131 例来自 Vero4DRT 的有限视野 CBCT 数据集进行训练（104 例数据集）和验证（27 例数据集）；其余的用于测试。当验证集中的 DSC 最大化时，训练例程将保存最佳权重值。在不同的测试数据集中，定性和定量评估了真实 ROI 和预测 ROI 之间的分割准确性。
在所有治疗机中，膀胱、前列腺、直肠和精囊的视觉评估平均得分±标准偏差分别为 1.0±0.7、1.5±0.6、1.4±0.6 和 2.1±0.8 分。所有成像设备的平均 DSC 值均≥0.94，用于膀胱；0.84-0.87，用于前列腺和直肠；0.48-0.69，用于精囊。尽管三个成像设备的膀胱 DSC 值存在显著差异，但膀胱的 DSC 值变化不到 1%。所有成像设备的平均 MSD 值均≤1.2mm，用于膀胱；1.4-2.2mm，用于前列腺、直肠和精囊。三个成像设备之间的精囊 MSD 值存在显著差异。
该方法对于测试来自训练数据集的具有不同强度分布和视野的数据集是有效的。
