[1] GUO Q, DUFFY S P, MATTHEWS K et al. Microfluidic analysis of red blood cell deformability[J]. Journal of Biomechanics, 47, 1767-1776(2014).

[2] CHEUNG C Y L, IKRAM M K, CHEN C et al. Imaging retina to study dementia and stroke[J]. Progress in Retinal and Eye Research, 57, 89-107(2017).

[3] YU L F, QIN Z, ZHUANG T M et al. A framework for hierarchical division of retinal vascular networks[J]. Neurocomputing, 392, 221-232(2020).

[4] FRAZ M M, WELIKALA R A, RUDNICKA A R et al. QUARTZ: quantitative analysis of retinal vessel topology and size – an automated system for quantification of retinal vessels morphology[J]. Expert Systems with Applications, 42, 7221-7234(2015).

[5] LI J, ZHANG T, ZHAO Y et al. MC-UNet: multimodule concatenation based on U-shape network for retinal blood vessels segmentation[J]. Computational Intelligence and Neuroscience, 2022, 9917691(2022).

[6] CHAUDHURI S, CHATTERJEE S, KATZ N et al. Detection of blood vessels in retinal images using two-dimensional matched filters[J]. IEEE Transactions on Medical Imaging, 8, 263-269(1989).

[7] ZANA F, KLEIN J C. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation[J]. IEEE Transactions on Image Processing, 10, 1010-1019(2001).

[8] [8] CHUTATAPE O, ZHENG L, KRISHNAN S M. Retinal blood vessel detection tracking by matched Gaussian Kalman filters[C]Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine Biology Society. Hong Kong, China: IEEE, 1998: 3144 − 3149.

[9] JI J, LU X C, LUO M et al. Parallel fully convolutional network for semantic segmentation[J]. IEEE Access, 9, 673-682(2021).

[10] YUAN H B, ZHU J J, WANG Q F et al. An improved DeepLab v3+ deep learning network applied to the segmentation of grape leaf black rot spots[J]. Frontiers in Plant Science, 13, 795410(2022).

[11] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[12] [12] RONNEBERGER O, FISCHER P, BROX T. U: convolutional wks f biomedical image segmentation[C]18th International Conference on Medical Image Computing ComputerAssisted Intervention. Munich, Germany: Springer, 2015: 234 − 241.

[13] [13] WANG B, QIU S, HE H G. Dual encoding U f retinal vessel segmentation[C]22nd International Conference on Medical Image Computing Computer Assisted Intervention. Shenzhen, China: Springer, 2019: 84 − 92.

[14] [14] YOO D, PARK S, LEE J Y, et al. Attention: aggregating weak directions f accurate object detection[C]2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015: 2659 − 2667.

[15] CHERUKURI V, KUMAR B G V, BALA R et al. Deep retinal image segmentation with regularization under geometric priors[J]. IEEE Transactions on Image Processing, 29, 2552-2567(2020).

[16] 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).

[17] [17] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning f image recognition[C]2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016: 770 − 778.

[18] [18] ZHANG S H, FU H Z, YAN Y G, et al. Attention guided wk f retinal image segmentation[C]22nd International Conference on Medical Image Computing Computer Assisted Intervention. Shenzhen, China: Springer, 2019: 797 − 805.

[19] GU Z W, CHENG J, FU H Z et al. CE-Net: context encoder network for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging, 38, 2281-2292(2019).

[20] JIN Q G, MENG Z P, PHAM T D et al. DUNet: a deformable network for retinal vessel segmentation[J]. Knowledge-Based Systems, 178, 149-162(2019).

[21] WU H S, WANG W, ZHONG J F et al. SCS-Net: a scale and context sensitive network for retinal vessel segmentation[J]. Medical Image Analysis, 70, 102025(2021).

[22] GU R, WANG G T, SONG T et al. CA-Net: comprehensive attention convolutional neural networks for explainable medical image segmentation[J]. IEEE Transactions on Medical Imaging, 40, 699-711(2021).

[23] SUN K X, XIN Y L, MA Y D et al. ASU-Net: U-shape adaptive scale network for mass segmentation in mammograms[J]. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 42, 4205-4220(2022).

[24] [24] RAHMAN M M, MARCULESCU R. Medical image segmentation via caded attention decoding[C]2023 IEEECVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa, HI, USA: IEEE, 2023: 6211 − 6220.

[25] [25] GUO J S, ZHOU H Y, WANG L S, et al. U2022: expling dynamics in nonisomphic architecture[EBOL]. [20221027] . https:doi.g10.48550arXiv.2210.15566.