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A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.
- Lutnick, Brendon;
- Manthey, David;
- Becker, Jan U;
- Ginley, Brandon;
- Moos, Katharina;
- Zuckerman, Jonathan E;
- Rodrigues, Luis;
- Gallan, Alexander J;
- Barisoni, Laura;
- Alpers, Charles E;
- Wang, Xiaoxin X;
- Myakala, Komuraiah;
- Jones, Bryce A;
- Levi, Moshe;
- Kopp, Jeffrey B;
- Yoshida, Teruhiko;
- Zee, Jarcy;
- Han, Seung Seok;
- Jain, Sanjay;
- Rosenberg, Avi Z;
- Jen, Kuang Yu;
- Sarder, Pinaki;
- Kidney Precision Medicine Project
- et al.
Abstract
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.Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.
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