Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different
|EI Accession Number|
Adaptive optics ; Aldehydes ; Classification (of information) ; Computer vision ; Decision making ; Image classification ; Image segmentation ; Learning systems ; Ophthalmology ; Optical tomography ; Signal encoding
|ESI Classification Code|
Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Light/Optics:741.1 ; Vision:741.2 ; Optical Devices and Systems:741.3 ; Organic Compounds:804.1 ; Information Sources and Analysis:903.1 ; Management:912.2
|ESI Research Field|
Cited Times [WOS]:1
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.Ningbo Institute of Materials Technology and Engineering, Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences, Ningbo, China
2.Department of Ophthalmology, Second Affiliated Hospital of Zhejiang University, China
3.Ningbo First Hospital, Ningbo, China
4.Department of Computer Science, Edge Hill University, Ormskirk, UK
5.School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo, China
6.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Hao，Jinkui,Shen，Ting,Zhu，Xueli,et al. Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2022,PP(99):1-1.
Hao，Jinkui.,Shen，Ting.,Zhu，Xueli.,Liu，Yonghuai.,Behera，Ardhendu.,...&Zhao，Yitian.(2022).Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning.IEEE TRANSACTIONS ON MEDICAL IMAGING,PP(99),1-1.
Hao，Jinkui,et al."Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning".IEEE TRANSACTIONS ON MEDICAL IMAGING PP.99(2022):1-1.
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