ArticleFrontiers in physiology2012
Pancreas++: automated quantification of pancreatic islet cells in microscopy images.
Article in Frontiers in physiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Unraveling the interplay between RAS axes and NOX signaling in fibroblasts during cardiac fibrosis.Molecular and cellular biochemistry · 2026Review
- PyCreas: a tool for quantification of localization and distribution of endocrine cell types in the islets of Langerhans.Frontiers in endocrinology · 2023Article
- Unravelling the potential of gut microbiota in sustaining brain health and their current prospective towards development of neurotherapeutics.Archives of microbiology · 2021Review
- Article
- Disulfiram Treatment Normalizes Body Weight in Obese Mice.Cell metabolism · 2020Article
- Absence of cannabinoid 1 receptor in beta cells protects against high-fat/high-sugar diet-induced beta cell dysfunction and inflammation in murine islets.Diabetologia · 2018Article
- A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- Stereological analyses of the whole human pancreas.Scientific reports · 2016Article
- Biphasic decline of β-cell function with age in euglycemic nonobese diabetic mice parallels diabetes onset.IUBMB life · 2015Article
- GIT2 Acts as a Systems-Level Coordinator of Neurometabolic Activity and Pathophysiological Aging.Frontiers in endocrinology · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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