  3D to 2D retinal vessel segmentation is a challenging problem in Optical
Coherence Tomography Angiography (OCTA) images. Accurate retinal vessel
segmentation is important for the diagnosis and prevention of ophthalmic
diseases. However, making full use of the 3D data of OCTA volumes is a vital
factor for obtaining satisfactory segmentation results. In this paper, we
propose a Progressive Attention-Enhanced Network (PAENet) based on attention
mechanisms to extract rich feature representation. Specifically, the framework
consists of two main parts, the three-dimensional feature learning path and the
two-dimensional segmentation path. In the three-dimensional feature learning
path, we design a novel Adaptive Pooling Module (APM) and propose a new
Quadruple Attention Module (QAM). The APM captures dependencies along the
projection direction of volumes and learns a series of pooling coefficients for
feature fusion, which efficiently reduces feature dimension. In addition, the
QAM reweights the features by capturing four-group cross-dimension
dependencies, which makes maximum use of 4D feature tensors. In the
two-dimensional segmentation path, to acquire more detailed information, we
propose a Feature Fusion Module (FFM) to inject 3D information into the 2D
path. Meanwhile, we adopt the Polarized Self-Attention (PSA) block to model the
semantic interdependencies in spatial and channel dimensions respectively.
Experimentally, our extensive experiments on the OCTA-500 dataset show that our
proposed algorithm achieves state-of-the-art performance compared with previous
methods.

Image and Video Processing;Computer Vision and Pattern Recognition