Affiliation:
1. Qufu Normal University
Abstract
Optical coherence tomography angiography (OCTA) has been widely used in clinical fields because of its noninvasive, high-resolution qualities. Accurate vessel segmentation on OCTA images plays an important role in disease diagnosis. Most deep learning methods are based on region segmentation, which may lead to inaccurate segmentation for the extremely complex curve structure of retinal vessels. We propose a U-shaped network called SS-Net that is based on the attention mechanism to solve the problem of continuous segmentation of discontinuous vessels of a retinal OCTA. In this SS-Net, the improved SRes Block combines the residual structure and split attention to prevent the disappearance of gradient and gives greater weight to capillary features to form a backbone with an encoder and decoder architecture. In addition, spatial attention is applied to extract key information from spatial dimensions. To enhance the credibility, we use several indicators to evaluate the function of the SS-Net. In two datasets, the important indicators of accuracy reach 0.9258/0.9377, respectively, and a Dice coefficient is achieved, with an improvement of around 3% compared to state-of-the-art models in segmentation.
Funder
Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology
Natural Science Foundation of Shandong Province
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering
Cited by
1 articles.
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