Automated segmentation of en face choroidal images obtained by optical coherent tomography by machine learning
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To develop an automated method to segment the choroidal layers of en face optical coherent tomography (OCT) images by machine learning.
A cross-sectional, prospective study of 276 eyes of 181 healthy subjects.
OCT 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.
The 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.
Automated stratification of the choroid in en face images can be done with high accuracy through machine learning.
KeywordsEn face OCT Choroid Machine learning Support vector machine
The authors thank Professor Emeritus Duco Hamasaki of the Bascom Palmer Eye Institute of the University of Miami for providing critical discussions and suggestions to our study and revision of the final manuscript. We also thank Mr. Eiichi Yoshimoto and Mr. Hiroshi Kiyota of Chinou Jouhou Shisutemu Inc. for technical assistance, and Dr. Masahiro Akiba of TOPCON corporation for valuable discussion. This study was supported by JSPS KAKENHI Grant number 15H04996.
Conflicts of interest
H Shiihara, None; S. Sonoda, None; H. Terasaki, None; N. Kakiuchi, None; Y. Shinohara, None; M. Tomita, None; T. Sakamoto, None.
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