PURPOSE: To develop an automated method to segment the choroidal layers of en face optical coherent tomography (OCT) images by machine learning. STUDY DESIGN: A cross-sectional, prospective study of 276 eyes of 181 healthy subjects. METHODS: 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. RESULTS: 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. CONCLUSION: Automated stratification of the choroid in en face images can be done with high accuracy through machine learning.
PURPOSE: To develop an automated method to segment the choroidal layers of en face optical coherent tomography (OCT) images by machine learning. STUDY DESIGN: A cross-sectional, prospective study of 276 eyes of 181 healthy subjects. METHODS: 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. RESULTS: 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. CONCLUSION: Automated stratification of the choroid in en face images can be done with high accuracy through machine learning.
Entities:
Keywords:
Choroid; En face OCT; Machine learning; Support vector machine
Authors: Lauren A Branchini; Mehreen Adhi; Caio V Regatieri; Namrata Nandakumar; Jonathan J Liu; Nora Laver; James G Fujimoto; Jay S Duker Journal: Ophthalmology Date: 2013-05-09 Impact factor: 12.079
Authors: Andrew Lang; Aaron Carass; Matthew Hauser; Elias S Sotirchos; Peter A Calabresi; Howard S Ying; Jerry L Prince Journal: Biomed Opt Express Date: 2013-06-14 Impact factor: 3.732