A Web Platform for Automated Image-Based Quantification of Human and Rodent Isolated Pancreatic Islets.

Authors

Sarah Suergiu, Ivan Leontovyc, Frantisek Saudek, David Habart

Published in

Journal of visualized experiments : JoVE. Issue 233. Jul 28, 2026. Epub Jul 28, 2026.

Abstract

The purpose of this publication is to provide a written and live-action video step-by-step protocol for standardized use of IsletNet, a web-based platform for automated quantification of isolated pancreatic islets in images, starting from sampling and image acquisition to image upload, visual validation, and automatic report generation. The platform was developed for standardized, automated assessment of two-dimensional microscopic images of isolated pancreatic islets to support clinical and experimental islet isolation workflows. Deep-learning-based image segmentation is used to identify islets and exocrine tissue from which contours are extracted for image analysis. Computational algorithms subsequently quantify islet count, volume, and purity. The visual Quality Control tool supports expert validation of automatic image analyses by allowing users to accept, manually correct, or exclude invalid images. The Clinical Islet Isolation module supports purity-fraction batch analysis and generates automatic PDF reports together with downloadable image and tabular data files for record keeping and analysis. The Simple Comparison module compares automated estimates with locally obtained reference results submitted with the corresponding sample images to evaluate the suitability of locally standardized images for automated analysis. The protocol also outlines recommendations for retraining using locally generated standardized images when necessary. Representative data from rodent and human islet isolations, including challenging examples, demonstrate improved automated analysis performance after appropriate training.

PMID:
42612131
Bibliographic data and abstract were imported from PubMed on 19 Aug 2026.

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