Segmentation of retinal vessels in fundus images based on U-Net with self-calibrated convolutions and spatial attention modules-Reference-Cited by-同舟云学术

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Segmentation of retinal vessels in fundus images based on U-Net with self-calibrated convolutions and spatial attention modules

Published:2023-03-10 Issue:7 Volume:61 Page:1745-1755
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Rong YiBiao,Xiong Yu,Li Chong,Chen Ying,Wei Peiwei,Wei Chuliang,Fan Zhun

Funder

science and technology planning project of guangdong province

project of educational commission of guangdong province of china

science research startup foundation of shantou university

state key lab of digital manufacturing equipment and technology

robot automatic design platform combining multi-objective evolutionary computation and deep neural network

Guangdong Natural Science Foundation

National Key R&D Program of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-023-02806-1.pdf

Reference32 articles.

1. Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK (2019) Recurrent residual U-Net for medical image segmentation. J Med Imaging 6(1):014,006

2. Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable cosfire filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57

3. Fan Z, Lu J, Wei C, Huang H, Cai X, Chen X (2018) A hierarchical image matting model for blood vessel segmentation in fundus images. IEEE Trans Image Process 28(5):2367–2377

4. Fazlali HR, Karimi N, Soroushmehr SR, Shirani S, Nallamothu BK, Ward KR, Samavi S, Najarian K (2018) Vessel segmentation and catheter detection in x-ray angiograms using superpixels. Med Biol Eng Comput 56(9):1515–1530

5. Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) Blood vessel segmentation methodologies in retinal images–a survey. Comput Methods Programs Biomed 108(1):407–433

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