SS-net: split and spatial attention network for vessel segmentation of retinal OCT angiography

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...

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Vydané v:Applied optics. Optical technology and biomedical optics Ročník 61; číslo 9; s. 2357
Hlavní autori: Jiang, Yingjie, Qi, Sumin, Meng, Jing, Cui, Baoyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 20.03.2022
Predmet:
Algorithms
Capillaries
Fluorescein Angiography
Retinal Vessels - diagnostic imaging
Tomography, Optical Coherence
ISSN:1539-4522, 2155-3165, 1539-4522
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1539-4522
2155-3165
1539-4522
DOI:10.1364/AO.451370