Show simple item record

dc.contributor.authorReschke, Melanie
dc.contributor.authorDiRito, Jenna R
dc.contributor.authorStern, David
dc.contributor.authorDay, Wesley
dc.contributor.authorPlebanek, Natalie
dc.contributor.authorHarris, Matthew
dc.contributor.authorHosgood, Sarah A
dc.contributor.authorNicholson, Michael L
dc.contributor.authorHaakinson, Danielle J
dc.contributor.authorZhang, Xuchen
dc.contributor.authorMehal, Wajahat Z
dc.contributor.authorOuyang, Xinshou
dc.contributor.authorPober, Jordan S
dc.contributor.authorSaltzman, W Mark
dc.contributor.authorTietjen, Gregory T
dc.date.accessioned2022-03-07T02:04:26Z
dc.date.available2022-03-07T02:04:26Z
dc.date.issued2022-01
dc.identifier.issn2380-6761
dc.identifier.otherPMC8780932
dc.identifier.other35111944
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334716
dc.description.abstractIn preclinical research, histological analysis of tissue samples is often limited to qualitative or semiquantitative scoring assessments. The reliability of this analysis can be impaired by the subjectivity of these approaches, even when read by experienced pathologists. Furthermore, the laborious nature of manual image assessments often leads to the analysis being restricted to a relatively small number of images that may not accurately represent the whole sample. Thus, there is a clear need for automated image analysis tools that can provide robust and rapid quantification of histologic samples from paraffin-embedded or cryopreserved tissues. To address this need, we have developed a color image analysis algorithm (DigiPath) to quantify distinct color features in histologic sections. We demonstrate the utility of this tool across multiple types of tissue samples and pathologic features, and compare results from our program to other quantitative approaches such as color thresholding and hand tracing. We believe this tool will enable more thorough and reliable characterization of histological samples to facilitate better rigor and reproducibility in tissue-based analyses.
dc.languageeng
dc.publisherWiley
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 101689146
dc.sourceessn: 2380-6761
dc.subjectHistology
dc.subjectImmunohistochemistry
dc.subjectColor Image Analysis
dc.subjectHuman Organ Research
dc.titleA digital pathology tool for quantification of color features in histologic specimens.
dc.typeArticle
dc.date.updated2022-03-07T02:04:25Z
prism.issueIdentifier1
prism.publicationNameBioeng Transl Med
prism.volume7
dc.identifier.doi10.17863/CAM.82134
dcterms.dateAccepted2021-07-18
rioxxterms.versionofrecord10.1002/btm2.10242
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidReschke, Melanie [0000-0003-1902-5876]
dc.identifier.eissn2380-6761
pubs.funder-project-idNational Institutes of Health (U01‐AI32895, R01‐HL085416, R01‐DK124420)
cam.issuedOnline2021-08-24


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International