Fig. 4: Interstitial fibrosis and tubular atrophy (IFTA) segmentation results—multi-institute study.

a Receiver operating characteristic (ROC) plots showing the segmentation performance of five trained IFTA models on 29 holdout whole slide images (WSIs), IFTATestSet 1. Models—Institution 1, Institution 2, and Institution 3 were trained using datasets from three different institutions (with 12, 24, and 12 WSIs respectively). The Combined full model was trained by pooling these three datasets (48 WSIs). The Combined 1/3rd model used 1/3rd of the pooled training set, randomly selected (16 WSIs). This last model yielded better IFTA segmentation performance than the first three models, highlighting the importance of dataset diversity. The combined full model offered slightly better performance than the Combined 1/3rd model. b shows the performance of the five models on the independent test dataset IFTATestSet 2 with 17 WSIs. This dataset originated from an independent institution than those used in [a] and was annotated by an independent annotator. We observed the same performance trend as in [a]. c shows the pairwise Intraclass correlation coefficients (ICC) (p value < 0.05) for percent IFTA scored visually by three additional annotators and estimated based on computational segmentation using the Combined full model (computer) for the 26 WSIs in KPMPTestSet. The kidney precision medicine project (KPMP) cohort acted as another independent test set which was never seen by our trained model. d shows computational IFTA predictions using the Combined full model on the holdout WSIs IFTATestSet 1. The left shows the traditional contour predictions, the right shows the corresponding heatmap predictions developed specifically for structures with poorly defined boundaries.