Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.
Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.
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DOI：
10.1002/mp.15521
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发表时间：
2022-04
期刊：
影响因子：
3.8
通讯作者：
Jordan, Petr
中科院分区：
文献类型：
--
作者：
Adamson, Philip M.;Bhattbhatt, Vrunda;Principi, Sara;Beriwal, Surabhi;Strain, Linda S.;Offe, Michael;Wang, Adam S.;Vo, Nghia-Jack;Schmidt, Taly Gilat;Jordan, Petr
本研究开发并评价了用于儿科CT器官分割的全卷积网络（FCN），并研究了FCN在图像异质性（如CT扫描仪模型协议和患者年龄）中的通用性。我们还评估了自动分割模型作为患者特定CT剂量估计软件工具的一部分。收集了359个具有专家器官轮廓的儿科CT数据集，用于模型开发和评价。使用改进的FCN 3D V-Net为每个器官训练自动分割模型。一个由60名患者组成的独立测试集被扣留进行测试。为了评价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训练单独的模型。将训练集和测试集分开，以回答有关儿科FCN自动分割模型对看不见的年龄组和扫描仪模型协议的可推广性的问题，以及扫描仪模型协议或年龄组特定模型的优点。最后，将自动分割模型产生的器官轮廓应用于患者特定剂量图，以评估分割误差对器官剂量估计的影响。结果表明，自动分割模型可推广到训练数据集中不存在的CT扫描仪采集和重建方法。虽然模型在不同年龄组之间并不具有同等的可推广性，但特定年龄组的模型并不比将不同年龄组组合成单个训练集具有任何优势。提供了19个器官结构的骰子相似系数（DSC）和平均表面距离结果，例如，中位DSC为0.52（十二指肠）、0.74（胰腺）、0.92（胃）和0.96（心脏）。FCN模型实现了所有19个器官的平均剂量误差在专家分割的5%以内，除了椎管，其平均误差为6.31%。总的来说，这些结果是有希望的FCN自动分割模型的儿科CT，包括应用程序的患者特定的CT剂量估计。
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. 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 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%. Overall these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT dose estimation.
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