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Error rate of automated choroidal segmentation using swept-source optical coherence tomography

Authors
Kong, M[Kong, Mingui]Eo, DR[Eo, Doo Ri]Han, G[Han, Gyule]Park, SY[Park, Sung Yong]Ham, DI[Ham, Don-Il]
Issue Date
Sep-2016
Publisher
WILEY-BLACKWELL
Keywords
automated choroidal segmentation; swept-source optical coherence tomography
Citation
ACTA OPHTHALMOLOGICA, v.94, no.6, pp.E427 - E431
Indexed
SCIE
SCOPUS
Journal Title
ACTA OPHTHALMOLOGICA
Volume
94
Number
6
Start Page
E427
End Page
E431
URI
https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/35465
DOI
10.1111/aos.12989
ISSN
1755-375X
Abstract
PurposeTo investigate the error rate of automated choroidal segmentation and the effect of frame averaging on error rate. MethodsA horizontal B scan at the fovea was performed in patients having various retinochoroidal disorders using swept-source optical coherence tomography (OCT) with frame-averaging technique. Scanned images were classified into four morphological groups: normal from fellow eyes (N-F), normal from pathologic eyes (N-P), retinal abnormality (R) and retinochoroidal abnormality (RC) group. Choroidal segmentation was automatically performed using built-in software of a swept-source OCT device, and the error rate of choroidal segmentation was analysed. ResultsQualified images for all four averaging types with different number of averaged frames were acquired in 89 eyes of 77 patients. Images of 12, 20, 24 and 33 eyes were classified as N-F, N-P, R and RC group, respectively. The choroidal segmentation error was detected in 1-2 images (8.3-16.7%) in the N-F group, 3-6 images (15.0-30.0%) in the N-P group, 4-8 images (16.7-33.3%) in the R group and 17-19 images (51.5-57.6%) in the RC group. The error rate was significantly higher in RC group than other groups (p<0.05). Increasing the number of frames for averaging showed no significant effect on the error rate in all groups (p>0.05). ConclusionAutomated choroidal segmentation showed a high error rate in images with choroidal abnormalities, and the averaging effect could not reduce the error rate significantly. Thus, further technological improvement is needed to increase the accuracy of the automated choroidal segmentation.
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