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Optical coherence tomography angiography (OCTA) has been widely used in ophthalmology in recent years due to its non-invasive and high resolution. In OCTA images, two biomarkers are extremely important for clinical diagnosis, i.e., foveal avascular zone (FAZ) and retinal vessel (RV), and RV has an implicit constraint on FAZ in position. In previous studies, the segmentation of the two biomarkers is naturally separated, which undoubtedly leads to the omission of such constraints between them. In this paper, we propose a joint segmentation framework (Joint-Seg), a single-encoder and dual-decoder architecture, through which simultaneous FAZ and RV extraction from en-face OCTA images can be achieved. Specifically, the OCTA image is encoded through joint encoding and provides FAZ- or RV-related information to separate decoding branches through a feature adaptive filter. In the FAZ segmentation branch, we propose a feature alignment decoder block (FADB) to recover image details, especially boundaries. While in the RV segmentation branch, a multi-scale soft fusion module (MSFM) is designed to adapt to different vessel thicknesses. Finally, we evaluate the proposed Joint-Seg on the OCTA-500 dataset, the experimental results show that our Joint-Seg outperforms the state-of-the-art methods on both FAZ and RV segmentation and has fewer FLOPs and parameters. The generalization experiments on four other datasets, i.e., OCTAGON, sFAZ, OCTA-25K, and ROSE also demonstrate the portability and scalability of the proposed Joint-Seg framework. In addition, the noise analysis further shows good robustness of the proposed method against noise.
Hu et al. (Sat,) studied this question.