Application of Artificial Intelligence and Deep Learning for Choroid Segmentation in Myopia.

PURPOSE: To investigate the correlation between choroidal thickness and myopia progression using a deep learning method. METHODS: Two data sets, data set A and data set B, comprising of 123 optical coherence tomography (OCT) volumes, were collected to establish the model and verify its clinical utility. The proposed mask region-based convolutional neural network (R-CNN) model, trained with the pretrained weights from the Common Objects in Context database as well as the manually labeled OCT images from data set A, was used to automatically segment the choroid. To verify its clinical utility, the mask R-CNN model was tested with data set B, and the choroidal thickness estimated by the model was also used to explore its relationship with myopia. RESULTS: Compared with the result of manual segmentation in data set B, the error of the automatic choroidal inner and outer boundary segmentation was 6.72 ± 2.12 and 13.75 ± 7.57 µm, respectively. The mean dice coefficient between the region segmented by automatic and manual methods was 93.87% ± 2.89%. The mean difference in choroidal thickness over the Early Treatment Diabetic Retinopathy Study zone between the two methods was 10.52 µm. Additionally, the choroidal thickness estimated using the proposed model was thinner in high-myopic eyes, and axial length was the most significant predictor. CONCLUSIONS: The mask R-CNN model has excellent performance in choroidal segmentation and quantification. In addition, the choroid of high myopia is significantly thinner than that of nonhigh myopia. TRANSLATIONAL RELEVANCE: This work lays the foundations for mask R-CNN models that could aid in the evaluation of more intricate changes occurring in chorioretinal diseases.

Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

PURPOSE: To report a rapid and accurate method based upon deep learning for automatic segmentation and measurement of the choroidal thickness (CT) in myopic eyes, and to determine the relationship between refractive error (RE) and CT. METHODS: Fifty-four healthy subjects 20-39 years of age were retrospectively reviewed. Data reviewed included age, gender, laterality, visual acuity, RE, and Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) images. The choroid layer was labeled by manual and automatic method using EDI-OCT. A Mask Region-convolutional Neural Network (Mask R-CNN) model, using deep Residual Network (ResNet) and Feature Pyramid Networks (FPN) as a backbone network, was trained to automatically outline and quantify the choroid layer. RESULTS: ResNet 50 model was adopted for its 90% accuracy rate and 6.97 s average execution time. CT determined by the manual method had a mean thickness of 258.75 ± 66.11 µm, a positive correlation with RE (r = 0.596, p < .01) and significant association with gender (p = .011) and RE (p < .001) in multivariable linear regression analysis. Meanwhile, CT determined by deep learning presented a mean thickness of 226.39 ± 54.65 µm, a positive correlation with RE (r = 0.546, p < .01) and significant association with gender (p = .043) and RE (p < .001) in multivariable linear regression analysis. Both methods revealed that CT decreased with the increase in myopic RE. CONCLUSIONS: This deep learning method using Mask-RCNN was able to successfully determine the relationship between RE and CT in an accurate and rapid way. It could eliminate the need for manual process, while demonstrating a feasible clinical application.