[1] Tian F, Li Y, Wang J. Retinal blood vessel segmentation based on multi-scale wavelet transform fusion[J]. Acta Optica Sinica, 41, 0410001(2021).

[2] Cai Z Z, Tang P, Hu J B et al. Retinal vessel segmentation based on PST and multi-scale Gaussian filter[J]. Application Research of Computers, 36, 1893-1896(2019).

[3] Jia H, Zheng C J, Li C B et al. Retinal blood vessel segmentation based on fuzzy C-means clustering according to the local line structural constraints[J]. Acta Optica Sinica, 40, 0910001(2020).

[4] Luo W J, Han G Q, Tian X D. Retinal vessel segmentation method based on multi-scale attention analytic network[J]. Laser & Optoelectronics Progress, 58, 2017001(2021).

[5] Wang L, Chen C X, Fu X et al. Retinal vessel segmentation of prematurity infants based on FDMU-net[J]. Laser & Optoelectronics Progress, 58, 1417003(2021).

[6] Cai Q H, Liu Y H, Zhang R F. Two-stage retinal vessel segmentation based on improved U-Net[J]. Laser & Optoelectronics Progress, 58, 1617002(2021).

[7] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[8] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[9] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[10] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[11] Jiang Y, Wu C, Wang G et al. MFI-Net: a multi-resolution fusion input network for retinal vessel segmentation[J]. PLoS One, 16, e0253056(2021).

[12] Zhang J, Chen Y, Bekkers E et al. Retinal vessel delineation using a brain-inspired wavelet transform and random forest[J]. Pattern Recognition, 69, 107-123(2017).

[13] Yan Z Q, Yang X, Cheng K T. Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation[J]. IEEE Transactions on Biomedical Engineering, 65, 1912-1923(2018).

[14] Lin Y, Zhang H G, Hu G. Automatic retinal vessel segmentation via deeply supervised and smoothly regularized network[J]. IEEE Access, 7, 57717-57724(2019).

[15] Yan Z Q, Yang X, Cheng K T. A three-stage deep learning model for accurate retinal vessel segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 23, 1427-1436(2019).

[16] Guo S, Wang K, Kang H et al. BTS-DSN: deeply supervised neural network with short connections for retinal vessel segmentation[J]. International Journal of Medical Informatics, 126, 105-113(2019).

[17] Lü X W, Shao F, Xiong Y M et al. Retinal vessel segmentation method based on two-stream networks[J]. Acta Optica Sinica, 40, 0410002(2020).

[18] Lü Y, Ma H, Li J N et al. Attention guided U-net with atrous convolution for accurate retinal vessels segmentation[J]. IEEE Access, 8, 32826-32839(2020).

[19] Tchinda B S, Tchiotsop D, Noubom M et al. Retinal blood vessels segmentation using classical edge detection filters and the neural network[J]. Informatics in Medicine Unlocked, 23, 100521(2021).

[20] Yang L, Wang H X, Zeng Q S et al. A hybrid deep segmentation network for fundus vessels via deep-learning framework[J]. Neurocomputing, 448, 168-178(2021).