Evidence map›Paper›PMID 23293605›Full record

ArticleFrontiers in physiology2012

Pancreas++: automated quantification of pancreatic islet cells in microscopy images.

Hongyu Chen, Bronwen Martin, Huan Cai, Jennifer L Fiori, Josephine M Egan, Sana Siddiqui, Stuart Maudsley

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. 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 · 2016
    Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Hongyu ChenReceptor Pharmacology Unit, Laboratory of Neuroscience, National Institute on Aging, National Institutes of Health Baltimore, MD, USA.
Bronwen Martin
Huan Cai
Jennifer L Fiori
Josephine M Egan
Sana Siddiqui
Stuart Maudsley

Funding

CLINICAL PROJECT: Aging And The PancreasZIAAG000214 · NIA · NATIONAL INSTITUTE ON AGING · PI EGAN, JOSEPHINE · 2009 to 2025
$4.8M
6 · The paper itself

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.

Indexed as

algorithmalpha-cellsbeta-cellsislets of Langerhanspancreasquantificationsoftware

Identifiers

PMID23293605
PMCPMC3535421

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.