  Most of the existing deep learning based methods for vessel segmentation
neglect two important aspects of retinal vessels, one is the orientation
information of vessels, and the other is the contextual information of the
whole fundus region. In this paper, we propose a robust Orientation and Context
Entangled Network (denoted as OCE-Net), which has the capability of extracting
complex orientation and context information of the blood vessels. To achieve
complex orientation aware, a Dynamic Complex Orientation Aware Convolution
(DCOA Conv) is proposed to extract complex vessels with multiple orientations
for improving the vessel continuity. To simultaneously capture the global
context information and emphasize the important local information, a Global and
Local Fusion Module (GLFM) is developed to simultaneously model the long-range
dependency of vessels and focus sufficient attention on local thin vessels. A
novel Orientation and Context Entangled Non-local (OCE-NL) module is proposed
to entangle the orientation and context information together. In addition, an
Unbalanced Attention Refining Module (UARM) is proposed to deal with the
unbalanced pixel numbers of background, thick and thin vessels. Extensive
experiments were performed on several commonly used datasets (DRIVE, STARE and
CHASEDB1) and some more challenging datasets (AV-WIDE, UoA-DR, RFMiD and UK
Biobank). The ablation study shows that the proposed method achieves promising
performance on maintaining the continuity of thin vessels and the comparative
experiments demonstrate that our OCE-Net can achieve state-of-the-art
performance on retinal vessel segmentation.
