DeepAI AI Chat
Log In Sign Up

PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation

08/26/2021
by   Zhuojie Wu, et al.
Beijing University of Posts and Telecommunications
ia.ac.cn
5

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.

READ FULL TEXT

page 1

page 3

page 4

page 7

04/07/2020

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

The precise segmentation of retinal blood vessel is of great significanc...
09/18/2020

Residual Spatial Attention Network for Retinal Vessel Segmentation

Reliable segmentation of retinal vessels can be employed as a way of mon...
12/14/2020

IPN-V2 and OCTA-500: Methodology and Dataset for Retinal Image Segmentation

Optical coherence tomography angiography (OCTA) is a novel imaging modal...
09/09/2018

Dual Attention Network for Scene Segmentation

In this paper, we address the scene segmentation task by capturing rich ...
07/25/2019

Attention Guided Network for Retinal Image Segmentation

Learning structural information is critical for producing an ideal resul...
11/23/2019

Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation

Recently 3D volumetric organ segmentation attracts much research interes...
10/17/2017

A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation

In this paper, we propose a new minimal path model for minimally interac...