Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81557
Title: Statistical framework for validation without ground truth of choroidal thickness changes detection
Authors: Ronchetti, T
Jud, C
Maloca, PM
Orgül, S
Giger, AT
Meier, C
Scholl, HPN
Chun, RKM 
Liu, Q 
To, CH 
Považay, B
Cattin, PC
Issue Date: 2019
Publisher: Public Library of Science
Source: PLoS one, 2019, v. 14, no. 6, e0218776 How to cite?
Journal: PLoS one 
Abstract: Monitoring 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.
URI: http://hdl.handle.net/10397/81557
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0218776
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.
The 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 athttps://doi.org/10.1371/journal.pone.0218776
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