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A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

Eisses, JF and Davis, AW and Tosun, AB and Dionise, ZR and Chen, C and Ozolek, JA and Rohde, GK and Husain, SZ (2014) A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. PLoS ONE, 9 (10).

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Abstract

© 2014 Eisses et al. The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operatordependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the ''ground truth''). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%±0.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%± 0.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Eisses, JFjfe11@pitt.eduJFE11
Davis, AW
Tosun, AB
Dionise, ZR
Chen, C
Ozolek, JAjao2@pitt.eduJAO2
Rohde, GK
Husain, SZsoh14@pitt.eduSOH140000-0001-9916-319X
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
EditorRakonczay, ZoltanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: 24 October 2014
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 9
Number: 10
DOI or Unique Handle: 10.1371/journal.pone.0110220
Schools and Programs: School of Medicine > Pathology
School of Medicine > Pediatrics
Refereed: Yes
Other ID: NLM PMC4208778
PubMed Central ID: PMC4208778
PubMed ID: 25343460
Date Deposited: 12 May 2015 18:25
Last Modified: 02 Feb 2019 23:55
URI: http://d-scholarship.pitt.edu/id/eprint/24035

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