Automated Segmentation of Kidney Cortex and Medulla in CT Images: A Multisite Evaluation Study

Significance Statement Volumetric measurements are needed to characterize kidney structural findings on CT images to evaluate and test their potential utility in clinical decision making. Deep learning can enable this task in a scalable and reliable manner. Although automated kidney segmentation has been previously explored, methods for distinguishing cortex from medulla have never been done before. In addition, automated methods are typically evaluated at a single institution, without testing generalizability and robustness across different institutions. The tool developed in this study performs at the level of human readers and could enable large diverse population studies to evaluate how kidney, cortex, and medulla volumes can be used in various clinical settings, and establish normative values at large scale. Background 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. Methods 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). Results 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. Conclusions 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.