  The lack of efficient segmentation methods and fully-labeled datasets limits
the 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 diseases
evaluation. Here, we introduce an innovative OCTA microstructure segmentation
network (OMSN) by combining an encoder-decoder-based architecture with
multi-scale skip connections and the split-attention-based residual network
ResNeSt, paying specific attention to OCTA microstructural features while
facilitating better model convergence and feature representations. The proposed
OMSN achieves excellent single/multi-task performances for RVN or/and FAZ
segmentation. Especially, the evaluation metrics on multi-task models
outperform single-task models on the same dataset. On this basis, a fully
annotated retinal OCTA segmentation (FAROS) dataset is constructed
semi-automatically, filling the vacancy of a pixel-level fully-labeled OCTA
dataset. OMSN multi-task segmentation model retrained with FAROS further
certifies its outstanding accuracy for simultaneous RVN and FAZ segmentation.
