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

Abstract

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.

Keywords: computed tomography; deep learning; kidney cortex; kidney medulla; kidney volume; machine learning collection; segmentation.

Figure 1.

Schematic illustration of the 3D U-Net architecture utilized in this study. The U-Net architecture consists of two pathways. The first combined downsampling with convolutional layers to encode the input. The second recombined these representations with shallower features transferred via skip connections to generate the final segmentation mask.

Figure 2.

Examples of automated segmentations. Red arrows indicate differences between the prediction and the ground truth. The dark blue and light blue masks correspond to the right cortex and medulla, respectively. The green and orange masks correspond to the left cortex and medulla, respectively. In the case depicted, the Dice coefficient calculated was approximately 0.90 for the cortex area and approximately 0.81 for the medulla region.

Figure 3.

High correlation and agreement for volume measurements of all four regions (right cortex, right medulla, left cortex, left medulla) obtained by the automated approach and manual segmentation in the Mayo Clinic Minnesota test set. The slope, Pearson coefficient, and P values are provided. Pearson coefficients close to zero imply no correlation.

Figure 4.

The agreement assessed using Bland–Altman, between the automated approach and manual segmentation of all four regions (right cortex, right medulla, left cortex, left medulla) in the Mayo Clinic Minnesota test set. Mean volumes along the x-axis are represented in cubic centimeters. The solid line represents the actual mean difference (bias), and the dotted lines show 95% limits of agreements (LoAs).

Figure 5.

Bland–Altman analysis of nine readers (blue circles) and the automated method (red squares) compared with the reference standard (mean of nine readers) for all four regions. Mean volumes along the x-axis are represented in cubic centimeters. The solid line represents the actual mean difference (bias), and the dotted lines show 95% LoAs. Both are calculated from the nine readers.