Deep learning segmentation of glomeruli on kidney donor frozen sections

Xiang Li, Richard C. Davis, Yuemei Xu, Zehan Wang, Nao Souma, Gina Sotolongo, Jonathan Bell, Matthew Ellis, David Howell, Xiling Shen, Kyle J. Lafata, Laura Barisoni

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic 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. Nonsclerotic (n = 22767) and sclerotic (n = 1366) glomeruli were manually annotated by study pathologists on all WSIs. A nine-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of nonsclerotic 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 nonsclerotic 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. We represent the first step toward new protocols for the evaluation of donor kidney biopsies.

Original languageEnglish (US)
Article number067501
JournalJournal of Medical Imaging
Volume8
Issue number6
DOIs
StatePublished - Nov 1 2021
Externally publishedYes

Keywords

  • deep learning
  • donor biopsy
  • frozen section
  • kidney allograft
  • segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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