Show simple item record

dc.contributor.authorKose, Kivanc
dc.contributor.authorBozkurt, Alican
dc.contributor.authorAlessi-Fox, Christi
dc.contributor.authorBrooks, Dana H
dc.contributor.authorDy, Jennifer G
dc.contributor.authorRajadhyaksha, Milind
dc.contributor.authorGill, Melissa
dc.date.accessioned2022-11-02T16:55:23Z
dc.date.available2022-11-02T16:55:23Z
dc.date.issued2019-12-12
dc.identifier.citationKose K, Bozkurt A, Alessi-Fox C, Brooks DH, Dy JG, Rajadhyaksha M, Gill M. Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy. J Invest Dermatol. 2020 Jun;140(6):1214-1222. doi: 10.1016/j.jid.2019.10.018. Epub 2019 Dec 12. PMID: 31838127; PMCID: PMC7967900.en_US
dc.identifier.eissn1523-1747
dc.identifier.doi10.1016/j.jid.2019.10.018
dc.identifier.pmid31838127
dc.identifier.urihttp://hdl.handle.net/20.500.12648/7838
dc.description.abstractIn vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is expanding from real-time diagnosis at the bedside to include a capture, store, and forward model with image interpretation and diagnosis occurring offsite, similar to radiology. As the patient may no longer be present at the time of image interpretation, quality assurance is key during image acquisition. Herein, we introduce a quality assurance process by means of automatically quantifying diagnostically uninformative areas within the lesional area by using RCM and coregistered dermoscopy images together. We trained and validated a pixel-level segmentation model on 117 RCM mosaics collected by international collaborators. The model delineates diagnostically uninformative areas with 82% sensitivity and 93% specificity. We further tested the model on a separate set of 372 coregistered RCM-dermoscopic image pairs and illustrate how the results of the RCM-only model can be improved via a multimodal (RCM + dermoscopy) approach, which can help quantify the uninformative regions within the lesional area. Our data suggest that machine learning-based automatic quantification offers a feasible objective quality control measure for RCM imaging.en_US
dc.language.isoenen_US
dc.relation.urlhttps://www.jidonline.org/article/S0022-202X(19)33477-3/fulltexten_US
dc.rightsCopyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleUtilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.en_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleThe Journal of investigative dermatologyen_US
dc.source.volume140
dc.source.issue6
dc.source.beginpage1214
dc.source.endpage1222
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.description.versionVoRen_US
refterms.dateFOA2022-11-02T16:55:23Z
dc.description.institutionSUNY Downstateen_US
dc.description.departmentPathologyen_US
dc.description.degreelevelN/Aen_US
dc.identifier.journalThe Journal of investigative dermatology


Files in this item

Thumbnail
Name:
Publisher version
Thumbnail
Name:
PIIS0022202X19334773.pdf
Size:
3.954Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.
Except where otherwise noted, this item's license is described as Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.