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

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

SUBMITTER: Lutnick B 

PROVIDER: S-EPMC9391340 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature