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 code, docker image, and a video overview of the tool are made freely available for the community.
Resources
- Instance in University at Buffalo
- Instance via Kitware Inc.
- Instance in University of Florida
- Instance via HuBMAP
- Documentation
- Set-Up Instructions
Select References
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.