ArticleJapanese journal of ophthalmology2018
Automated segmentation of en face choroidal images obtained by optical coherent tomography by machine learning.
Article in Japanese journal of ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Trial
- Deep learning-based automated classification of choroidal layers in en face swept-source optical coherence tomography images.BMC ophthalmology · 2025Article
- Intervortex Venous Anastomosis in the Macula in Central Serous Chorioretinopathy Imaged by En Face Optical Coherence Tomography.Journal of clinical medicine · 2023Article
- Choroidal imaging using optical coherence tomography: techniques and interpretations.Japanese journal of ophthalmology · 2022Review
- Running pattern of choroidal vessel in en face OCT images determined by machine learning-based quantitative method.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
purposeTo develop an automated method to segment the choroidal layers of en face optical coherent tomography (OCT) images by machine learning. STUDY
designA cross-sectional, prospective study of 276 eyes of 181 healthy subjects.
methodsOCT en face images of the choroid were obtained every 2.6 μm from the retinal pigment epithelium (RPE) to the chorioscleral border. The images at the start of the choriocapillaris, start of Sattler's layer, and start of Haller's layer were identified, and the image numbers from the RPE line were taken as the teacher data. Forty-one feature quantities of each image were extracted. A support vector machine (SVM) model was created from each feature value of the training data, and a coefficient of determination was calculated for each layer of the choroid by a fivefold cross validation. Next, the same evaluation was performed after creating a SVM model with selected effective feature quantities.
resultsThe mean coefficient of determination using all features was 0.9853 ± 0.0012. Nine effective feature quantities (relative choroid thickness, mean/kurtosis/variance of brightness, FFT_ skewness, k0_vessel width, k1/k2/k4_vessel area) were selected, and the mean of the coefficient of determinations with these quantities In this model was 0.9865 ± 0.0001. The number of errors in the image number at the start of each layer was 1.01 ± 0.79 for the choriocapillaris, 1.13 ± 1.12 for Sattler's layer, and 3.77 ± 2.90 for Haller's layer.
conclusionAutomated stratification of the choroid in en face images can be done with high accuracy through machine learning.
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