Title
Pancreas++ : automated quantification of pancreatic islet cells in microscopy images Pancreas++ : automated quantification of pancreatic islet cells in microscopy images
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
[Lausanne] :Frontiers Research Foundation ,
Subject
Human medicine
Source (journal)
Frontiers in physiology / Frontiers Research Foundation (Lausanne, Switzerland) - [Lausanne], 2010, currens
Volume/pages
3(2012) , p. 1-9
ISSN
1664-042X
Article Reference
482
Carrier
E-only publicatie
Target language
English (eng)
Full text (Publishers DOI)
Abstract
The microscopic image analysis of pancreatic Islet of Langerhans morphology is crucial for the investigation of diabetes and metabolic diseases. Besides the general size of the islet, the percentage and relative position of glucagon-containing alpha-, and insulin-containing beta-cells is also important for pathophysiological analyses, especially in rodents. Hence, the ability to identify, quantify and spatially locate peripheral, and involuted alpha-cells in the islet core is an important analytical goal. There is a dearth of software available for the automated and sophisticated positional quantification of multiple cell types in the islet core. Manual analytical methods for these analyses, while relatively accurate, can suffer from a slow throughput rate as well as user-based biases. Here we describe a newly developed pancreatic islet analytical software program, Pancreas++, which facilitates the fully automated, non-biased, and highly reproducible investigation of islet area and alpha- and beta-cell quantity as well as position within the islet for either single or large batches of fluorescent images. We demonstrate the utility and accuracy of Pancreas++ by comparing its performance to other pancreatic islet size and cell type (alpha, beta) quantification methods. Our Pancreas++ analysis was significantly faster than other methods, while still retaining low error rates and a high degree of result correlation with the manually generated reference standard.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/bb6f64/132153.pdf