  Segmenting specific targets or structures in optical coherence tomography
angiography (OCTA) images is fundamental for conducting further pathological
studies. The retinal vascular layers are rich and intricate, and such vascular
with complex shapes can be captured by the widely-studied OCTA images. In this
paper, we thus study how to use OCTA images with projection vascular layers to
segment retinal structures. To this end, we propose the SSW-OCTA model, which
integrates the advantages of deformable convolutions suited for tubular
structures and the swin-transformer for global feature extraction, adapting to
the characteristics of OCTA modality images. Our model underwent testing and
comparison on the OCTA-500 dataset, achieving state-of-the-art performance. The
code is available at: https://github.com/ShellRedia/Snake-SWin-OCTA.
