Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation-Reference-Cited by-同舟云学术

Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation

Published:2022-08 Issue:8 Volume:41 Page:1975-1989
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Li Yang¹^ORCID,Zhang Yue¹,Cui Weigang¹^ORCID,Lei Baiying²^ORCID,Kuang Xihe³^ORCID,Zhang Teng³^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

2. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University, Shenzhen, China

3. Department of Orthopaedics and Traumatology, Faculty of Medicine, The University of Hong Kong, Hong Kong

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Beijing United Imaging Research Institute of Intelligent Imaging Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/9847115/09714302.pdf?arnumber=9714302

Reference77 articles.

1. A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images

2. An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation

3. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

4. Ridge-Based Vessel Segmentation in Color Images of the Retina

5. Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification

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