Abstract：
In kidney transplantation, a contrast CT scan is obtained in the donor candidate to detect subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney, cortex, and medulla volumes using a manual image-processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measurements are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes.
A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained (n=1238) and validated (n=306), and then evaluated in a hold-out test set of reference standard segmentations (n=386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites (n=1226).
The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets.
A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.
A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained (n=1238) and validated (n=306), and then evaluated in a hold-out test set of reference standard segmentations (n=386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites (n=1226).
The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets.
A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.
摘要：
在肾移植中，在供体候选人中获得对比CT扫描以检测肾脏的亚临床病理。衰老肾脏解剖学研究的最新工作对肾脏进行了表征，皮质,和髓质体积使用手动图像处理工具。然而,这种技术对于临床护理来说既耗时又不切实际，因此，这些测量不是在捐赠者评估期间获得的。这项研究提出了一种全自动分割方法来测量肾脏，皮质,和髓质卷。
使用来自一个机构的参考标准手动分割的总共1930次对比增强CT检查来开发算法。对卷积神经网络模型进行了训练(n=1238)和验证(n=306)，然后在参考标准分段的保持测试集中进行评估（n=386）。经过初步评估,该算法在来自两个外部站点(n=1226)的数据集上进一步测试。
发现自动化模型的性能与手动分割相当，具有类似于人工分割的观察者间变异性的误差。与参考标准相比，自动化方法实现了0.94的骰子相似性度量(右皮质)，0.90（右髓质），0.94（左皮质），和0.90（左髓质）在测试集中。当将该算法应用于两个外部数据集时,观察到类似的性能。
已经建立了一种用于测量肾脏CT图像中皮质和髓质体积的全自动方法。该方法可证明可用于广泛的临床应用。
使用来自一个机构的参考标准手动分割的总共1930次对比增强CT检查来开发算法。对卷积神经网络模型进行了训练(n=1238)和验证(n=306)，然后在参考标准分段的保持测试集中进行评估（n=386）。经过初步评估,该算法在来自两个外部站点(n=1226)的数据集上进一步测试。
发现自动化模型的性能与手动分割相当，具有类似于人工分割的观察者间变异性的误差。与参考标准相比，自动化方法实现了0.94的骰子相似性度量(右皮质)，0.90（右髓质），0.94（左皮质），和0.90（左髓质）在测试集中。当将该算法应用于两个外部数据集时,观察到类似的性能。
已经建立了一种用于测量肾脏CT图像中皮质和髓质体积的全自动方法。该方法可证明可用于广泛的临床应用。
