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The lack of efficient segmentation methods and fully-labeled datasets limitsthe comprehensive assessment of optical coherence tomography angiography (OCTA)microstructures like retinal vessel network (RVN) and foveal avascular zone(FAZ), which are of great value in ophthalmic and systematic diseasesevaluation. Here, we introduce an innovative OCTA microstructure segmentationnetwork (OMSN) by combining an encoder-decoder-based architecture withmulti-scale skip connections and the split-attention-based residual networkResNeSt, paying specific attention to OCTA microstructural features whilefacilitating better model convergence and feature representations. The proposedOMSN achieves excellent single/multi-task performances for RVN or/and FAZsegmentation. Especially, the evaluation metrics on multi-task modelsoutperform single-task models on the same dataset. On this basis, a fullyannotated retinal OCTA segmentation (FAROS) dataset is constructedsemi-automatically, filling the vacancy of a pixel-level fully-labeled OCTAdataset. OMSN multi-task segmentation model retrained with FAROS furthercertifies its outstanding accuracy for simultaneous RVN and FAZ segmentation.

OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset