A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology

the Kidney Precision Medicine Project

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Background: Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods: We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results: By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions: Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.

Original languageEnglish (US)
Article number105
JournalCommunications Medicine
Issue number1
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health
  • Internal Medicine
  • Epidemiology
  • Medicine (miscellaneous)
  • Assessment and Diagnosis


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