FOVEAL AVASCULAR ZONE SEGMENTATION OF OCTA IMAGES USING DEEP LEARNING APPROACH WITH UNSUPERVISED VESSEL SEGMENTATION

Foveal Avascular Zone (FAZ) is a crucial indicator for retinal disease detection and accurate automatic FAZ segmentation has a significant impact in clinical applications. Apart from the binary FAZ segmentation map, a vessel segmentation map can provide further information. To simultaneously implement vessel and accurate FAZ segmentation, an end-to-end trained network is proposed to achieve unsupervised vessel segmentation and supervised FAZ segmentation. Due to the lack of vessel labels, the style transfer with consistency loss is proposed to the vessel segmentation. Then FAZ segmentation is achieved with a U-Net structure based on vessel segmentation. Two superficial layer OCTA image datasets - OCTAGON3 [1] and sFAZDATA datasets [2] - are used to evaluate the proposed method. We achieve the Dice scores of 0.9263 and 0.9784, which are better than those from other approaches.