Development of a deep learning model for glomerulus detection and segmentation in kidney histopathology images
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Background
Estimation of nephron number, an indicator of renal reserve, requires quantitative assessment of glomerular number and size in kidney biopsy specimens. We aimed to develop an automated system for quantitative measurement of glomerular areas from clinical kidney biopsy images, across diverse kidney diseases.
Methods
We developed a two-stage deep learning system combining Faster R-CNN for glomerular detection in whole-slide images (WSIs) and DeepLabV3+ for segmentation. Of 114 kidney biopsies (228 WSIs), 104 were used for model development and 10 for independent final evaluation. Agreement was assessed using the F1-score for detection, the Dice coefficient for segmentation, and the intraclass correlation coefficient (ICC) for glomerular area measurement. Analysis was performed at 40× total magnification (2.331 μm/pixel) and validated against 400× images.
Results
In the independent evaluation set, the artificial intelligence (AI) model achieved a mean F1-score of 0.851 for detection, compared with 0.888 among experts. For segmentation, the Dice coefficient was 0.959 ± 0.011 and the per-pair ICC (2,1) for area measurement was 0.989 ± 0.009; both were comparable to inter-expert agreement. Bland–Altman analysis showed a mean bias of −344 μm² (95% limits of agreement, −1904 to 1216 μm²) between 40× and 400× measurements.
Conclusion
The two-stage model achieved glomerular segmentation and area measurement comparable to expert agreement in independent clinical data, with detection performance approaching inter-expert agreement, and provided reliable measurements at low magnification. This automated approach may facilitate large-scale quantitative kidney pathology and nephron number estimation from clinical kidney biopsies.
