Automatic Foveal Avascular Zone Segmentation Using Hessian-Based Filter and U-Net Deep Learning Network

Accurate segmentation of Foveal Avascular Zone (FAZ) in Optical Coherence Tomography Angiography (OCTA) images is important for OCTA images analysis. In this work, we developed an algorithm for automatic segmentation of FAZ in OCTA images using a Hessian-based filter and an U-Net deep learning network. A total of 260 OCTA images were used to train and test the algorithm. The images were first enhanced by a Hessian-based filter and then fed into a U-Net deep learning network. Eighty percent of the dataset was used for training and twenty percent was used for testing. Our method achieved 87.8% Jaccard Index (Intersection over Union metric) with 6% false-negative error and 5% false-positive error. The results showed that U-Net deep learning network could achieve good accuracy in automatically segmenting FAZ in OCTA images despite a small training set. The study also showed that image preprocessing techniques such as Hessian-based filtering helped to improve accuracy of U-Net deep learning network.