技术说明：用于儿科 CT 扫描的 V-Net 自动分割算法的评估：性能、泛化能力以及在患者特定 CT 剂量学中的应用。
Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.
机构信息
Varian Medical Systems, Palo Alto, California, USA.
Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
出版信息
Med Phys. 2022 Apr;49(4):2342-2354. doi: 10.1002/mp.15521. Epub 2022 Feb 22.
PURPOSE
This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation.
METHODS
A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-group-specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ dose estimation.
RESULTS
Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%.
CONCLUSIONS
Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT dose estimation.
目的
本研究开发并评估了一种用于儿科 CT 器官分割的全卷积网络（FCN），并研究了 FCN 在 CT 扫描仪模型协议和患者年龄等图像异质性方面的泛化能力。我们还评估了自动分割模型，作为用于患者特定 CT 剂量估计的软件工具的一部分。
方法
使用专家器官轮廓的 359 个儿科 CT 数据集进行模型开发和评估。使用修改后的 FCN 3D V-Net 为每个器官训练自动分割模型。保留 60 名患者的独立测试集进行测试。为了评估 CT 扫描仪模型协议和患者年龄异质性的影响，使用扫描仪模型协议和儿科年龄组的子集分别训练模型。将训练集和测试集分开，以回答关于儿科 FCN 自动分割模型对未见年龄组和扫描仪模型协议的泛化能力的问题，以及扫描仪模型协议或年龄组特定模型的优点。最后，将自动分割模型生成的器官轮廓应用于患者特定的剂量图，以评估分割误差对器官剂量估计的影响。
结果
结果表明，自动分割模型可以推广到训练数据集中不存在的 CT 扫描仪采集和重建方法。虽然模型在年龄组之间的泛化能力并不相同，但年龄组特定的模型并没有比将不同年龄组组合到一个单一的训练集中更具优势。对于 19 个器官结构，给出了 Dice 相似系数（DSC）和平均表面距离结果，例如，十二指肠、胰腺、胃和心脏的中位数 DSC 分别为 0.52、0.74、0.92 和 0.96。FCN 模型除了椎管外，对于所有 19 个器官，平均剂量误差均在专家分割的 5%以内，其中平均误差为 6.31%。
结论
总的来说，这些结果为在儿科 CT 中采用 FCN 自动分割模型，包括在患者特定 CT 剂量估计中的应用，提供了有希望的结果。
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