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dc.contributor.authorRonchetti, Ten_US
dc.contributor.authorJud, Cen_US
dc.contributor.authorMaloca, PMen_US
dc.contributor.authorOrgül, Sen_US
dc.contributor.authorGiger, ATen_US
dc.contributor.authorMeier, Cen_US
dc.contributor.authorScholl, HPNen_US
dc.contributor.authorChun, RKMen_US
dc.contributor.authorLiu, Qen_US
dc.contributor.authorTo, CHen_US
dc.contributor.authorPovažay, Ben_US
dc.contributor.authorCattin, PCen_US
dc.identifier.citationPLoS one, 2019, v. 14, no. 6, e0218776en_US
dc.description.abstractMonitoring subtle choroidal thickness changes in the human eye delivers insight into the pathogenesis of various ocular diseases such as myopia and helps planning their treatment. However, a thorough evaluation of detection-performance is challenging as a ground truth for comparison is not available. Alternatively, an artificial ground truth can be generated by averaging the manual expert segmentations. This makes the ground truth very sensitive to ambiguities due to different interpretations by the experts. In order to circumvent this limitation, we present a novel validation approach that operates independently from a ground truth and is uniquely based on the common agreement between algorithm and experts. Utilizing an appropriate index, we compare the joint agreement of several raters with the algorithm and validate it against manual expert segmentation. To illustrate this, we conduct an observational study and evaluate the results obtained using our previously published registration-based method. In addition, we present an adapted state-of-the-art evaluation method, where a paired t-test is carried out after leaving out the results of one expert at the time. Automated and manual detection were performed on a dataset of 90 OCT 3D-volume stack pairs of healthy subjects between 8 and 18 years of age from Asian urban regions with a high prevalence of myopia.en_US
dc.description.sponsorshipSchool of Optometryen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPLoS oneen_US
dc.rights© 2019 Ronchetti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rightsThe following publication Ronchetti T, Jud C, Maloca PM, Orgül S, Giger AT, Meier C, et al. (2019) Statistical framework for validation without ground truth of choroidal thickness changes detection. PLoS ONE 14(6): e0218776, is available at
dc.titleStatistical framework for validation without ground truth of choroidal thickness changes detectionen_US
dc.typeJournal/Magazine Articleen_US
dc.description.validate201910 bcma-
Appears in Collections:Journal/Magazine Article
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