We developed a cloud-based end-user tool for large scale digital pathology WSI visualization, training deep learning models for detection and segmentation of tissue micro-compartments, and quantification of relevant structures. The tool is made available for the community via our cloud-based instance – HistoCloud.  The source codedocker image, and a video overview of the tool are made freely available for the community.

The user interface of HistoCloud. 
The user interface of HistoCloud. 


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B. Lutnick, D. Manthey, J. U. Becker, B. Ginley, K. Moos, J. E. Zuckerman, L. Rodrigues, A. J. Gallan, L. Barisoni, C. E. Alpers, X. X. Wang, K. Myakala, B. A. Jones, M. Levi, J. B. Kopp, T. Yoshida, S. S. Han, S. Jain, A. Z. Rosenberg, K. Y. Jen, and P. Sarder, for the Kidney Precision Medicine Project, “A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology,” Communications Medicine (London), vol. 2, pp. 105: 1-15, Aug. 2022.

Underline indicates corresponding author.
* indicates equal contribution.
§ indicates post doctoral associates.
indicates Dr. Sarder’s graduate students.
indicates Dr. Sarder’s undergraduate Students.