Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Abstract

This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI: 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI: 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P < .001), from 1724 seconds (95% CI: 1373, 2075) to 723 seconds (95% CI: 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time. Keywords: Convolutional Neural Network (CNN), Segmentation, Kidney ClinicalTrials.gov identification no.: NCT00792155 Supplemental material is available for this article. © RSNA, 2022.

Keywords: Convolutional Neural Network (CNN); Kidney; Segmentation.

Conflict of interest statement

Disclosures of Conflicts of Interest: A.G. No relevant relationships. G.S. Leadership roles as co-chair of Society for Imaging Informatics in Medicine machine learning committee, co-chair for Society of Abdominal Radiology AI committee, co-director of Radiological Society of North America AI certificate course; assistant editor of Radiology: Artificial Intelligence. S.R. No relevant relationships. S.J. No relevant relationships. H.D. No relevant relationships. R.H. No relevant relationships. D.R. No relevant relationships. K.T. No relevant relationships. J.D.B. Grants or contracts from Vertex Pharmaceuticals; secretary of American Journal of Hypertension. I.B. No relevant relationships. I.C. No relevant relationships. H.R. No relevant relationships. M.R.P. No relevant relationships.

2022 by the Radiological Society of North America, Inc.

Figures

Figure 1:

Schematic summarizes project infrastructure on deep learning server. Training is highlighted with a light pink background. Deployment and inference are highlighted with a light blue background.

Figure 2:

External dataset (top) and prospective dataset (bottom) validation with Bland-Altman agreement analysis and Dice similarity coefficient by htTKV. BA = Bland-Altman, htTKV = TKV indexed to patient height, TKV = total kidney volume.

Figure 3:

Prospective dataset (top row) and external dataset (bottom row) example surface renderings of ground truth reference (red) and model prediction (blue) volumes with 50% opacity. Overlapping concordant predictions are visualized in shades of purple. Yellow mannequin illustrates orientation of the surface renderings.

Figure 4:

Examples of the most significant prospective model inference errors and the corresponding radiologist corrections. Inference label is red, radiologist additions are green, and radiologist subtractions are indicated by blue arrows. (A) Fluid-filled stomach partially labeled as left cystic kidney. (B) Urinary bladder labeled as cystic kidney. (C) Liver cyst labeled as kidney. (D) Renal cyst at liver border missed by inference.(E) Complex hemorrhagic left renal cyst incompletely labeled. (F) Collapsed descending colon labeled as left kidney. (G) Renal cyst at liver border missed by inference.(H) Left elbow medial epicondyle fat labeled as left kidney in a patient imaged with arms in the field of view.