Fovea serves to be one of the crucial landmarks of the retina. The automatic detection of the foveal center in optical coherence tomography (OCT) images helps in diagnosing retinal diseases. However, challenges arise due to retinal structure damage and the demand for high time performance. In this study, we propose a fast and robust fovea detection framework for OCT and OCT angiography (OCTA) images. We focus on detecting the foveal center based on the foveal avascular zone (FAZ) segmentation. Firstly, the proposed framework uses a lightweight neural network to quickly segment the FAZ. Further, the geometric center of the FAZ is identified as the position of the foveal center. We validate the framework’s performance using two datasets. Dataset A contains two modalities of images from 316 subjects. Dataset B contains OCT data of 700 subjects with healthy eyes, choroidal neovascularization, geographic atrophy, and diabetic retinopathy. The Dice score of the FAZ segmentation is 84.68%, which is higher than that of the existing algorithms. The success rate (< 750 µm) and distance error of fovea detection in OCTA images are 100% and 92.3 ± 90.9 µm, respectively, which are better than that in OCT. For different disease situations, our framework is more robust than the existing algorithms and requires an average time of 0.02 s per eye. This framework has the potential to become an efficient and robust clinical tool for fovea detection in OCT images.

No abstract

No abstract

No abstract