Purpose: Recent advances in computational image analysis offer the opportunity to develop
automatic quantification of histologic parameters as aid tools for practicing pathologists. This work
aims to develop deep learning (DL) models to quantify non-sclerotic and sclerotic glomeruli on
frozen sections from donor kidney biopsies.
Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies
performed at our institution (n=123) and at external institutions (n=135) were used in this study.
WSIs from our institution were divided at the patient level into training and validation datasets
(Ratio: 0.8:0.2) and external WSIs were used as an independent testing dataset. Non-sclerotic
(n=22767) and sclerotic (n=1366) glomeruli were manually annotated by study pathologists on all
WSIs. A 9-layer convolutional neural network based on the common U-Net architecture was
developed and tested for the segmentation of non-sclerotic and sclerotic glomeruli. DL-derived,
manual segmentation and reported glomerular count (standard of care) were compared.
Results: The average Dice Similarity Coefficient testing was 0.90 and 0.83. and the F1, Recall,
and Precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for non-sclerotic and
sclerotic glomeruli, respectively. DL-derived and manual segmentation derived glomerular counts
were comparable, but statistically different from reported glomerular count.
Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of
glomeruli. This work represents the first step toward new protocols for the evaluation of donor
kidney biopsies.
Deep learning segmentation of glomeruli on kidney donor frozen sections
