ArticleJournal of neuroscience methods2021
Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans.
Article in Journal of neuroscience methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep learning-based automated classification of choroidal layers in en face swept-source optical coherence tomography images.BMC ophthalmology · 2025Article
- Cardiac Adipose Tissue Segmentation via Image-Level Annotations.IEEE journal of biomedical and health informatics · 2023Article
- Choroid automatic segmentation and thickness quantification on swept-source optical coherence tomography images of highly myopic patients.Annals of translational medicine · 2022Article
- Deep-learning algorithms for choroidal thickness measurements in high myopia.Annals of translational medicine · 2022Article
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11 authors.
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Abstract
backgroundChanges in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT). NEW
methodEighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC).
resultsThe mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/- 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977-0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977-0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively. COMPARISON WITH EXISTING METHOD(S): The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability.
conclusionsThis automated segmentation method can reliably measure choroidal thickness using different OCT platforms.
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