[1] WU Y C, XIA Y, SONG Y, et al. Multiscale wk followed wk model f retinal vessel segmentation[C]Proceedings of the 21st International Conference on Medical Image Computing ComputerAssisted Intervention. Granada: Springer, 2018: 119 − 126.

[2] PLAYOUT C, DUVAL R, CHERIET F. A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images[J]. IEEE Transactions on Medical Imaging, 38, 2434-2444(2019).

[3] HOOVER A D, KOUZNETSOVA V, GOLDBAUM M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response[J]. IEEE Transactions on Medical Imaging, 19, 203-210(2000).

[4] 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).

[5] 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. Vol. 20 Biomedical Engineering Towards the Year 2000 Beyond. Hong Kong, China: IEEE, 1998: 3144 − 3149.

[6] YANG L, LI E, LONG T, et al. A welding quality detection method for arc welding robot based on 3D reconstruction with SFS algorithm[J]. The International Journal of Advanced Manufacturing Technology, 94, 1209-1220(2018).

[7] YANG L, LI E, FAN J F, et al. Automatic extraction and identification of narrow butt joint based on ANFIS before GMAW[J]. The International Journal of Advanced Manufacturing Technology, 100, 609-622(2019).

[8] BARLOW H B. Unsupervised learning[J]. Neural Computation, 1, 295-311(1989).

[9] YAMBAL M, GUPTA H. Image segmentation using fuzzy C means clustering: a survey[J]. International Journal of Advanced Research in Computer and Communication Engineering, 2, 2927-2929(2013).

[10] ANTER A M, HASSENIAN A E. CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm[J]. Artificial Intelligence in Medicine, 97, 105-117(2019).

[11] YOU X E, PENG Q M, YUAN Y, et al. Segmentation of retinal blood vessels using the radial projection and semi-supervised approach[J]. Pattern Recognition, 44, 2314-2324(2011).

[12] REINA A V, GELBART M, HUANG D, et al. Segmentation fusion f connectomics[C]Proceedings of 2011 International Conference on Computer Vision. Barcelona: IEEE, 2014: 177 − 184.

[13] FAN D P, ZHOU T, JI G P, et al. Inf-Net: automatic COVID-19 lung infection segmentation from CT images[J]. IEEE Transactions on Medical Imaging, 39, 2626-2637(2020).

[14] CHEN J N, LU Y Y, YU Q H, et al. TransU: transfmers make strong encoders f medical image segmentation[EBOL]. (2021–02–08).[2022–08–15] https:arxiv.gabs2102.04306.

[15] CIREAN D C, GIUSTI A, GAMBARDELLA L M, et al. Deep neural wks segment neuronal membranes in electron microscopy images[C]Proceedings of the 25th International Conference on Neural Infmation Processing Systems. Lake Tahoe: Curran Associates Inc. , 2012: 2843 − 2851.

[16] LONG J, SHELHAMER E, DARRELL T. Fully convolutional wks f semantic segmentation[C]Proceedings of 2015 IEEE Conference on Computer Vision Pattern Recognition. Boston: IEEE, 2015: 3431 − 3440.

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

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

[19] ZHOU Z W, SIDDIQUEE M R, TAJBAKHSH N, et al. UNet++: redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE Transactions on Medical Imaging, 39, 1856-1867(2020).

[20] 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).

[21] HU J, SHEN L, SUN G. Squeezeexcitation wks[C]Proceedings of 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City: IEEE, 2018: 7132 − 7141.

[22] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]Proceedings of the 15th European Conference on Computer Vision. Munich: Springer, 2018: 3 − 19.

[23] SAMUEL P M, VEERAMALAI T. Multilevel and multiscale deep neural network for retinal blood vessel segmentation[J]. Symmetry, 11, 946(2019).

[24] WANG B, QIU S, HE H G. Dual encoding u f retinal vessel segmentation[C]Proceedings of the 22nd International Conference on Medical Image Computing ComputerAssisted Intervention. Shenzhen: Springer, 2019: 84 − 92.

[25] 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).

[26] WU Y C, XIA Y, SONG Y, et al. Vessel: retinal vessel segmentation under multipath supervision[C]Proceedings of the 22nd International Conference on Medical Image Computing ComputerAssisted Intervention. Shenzhen: Springer, 2019: 264 − 272.

[27] GUO C L, SZEMENYEI M, YI Y G, et al. SAU: spatial attention U f retinal vessel segmentation[C]25th International Conference on Pattern Recognition. Milan: IEEE, 2020: 1236 − 1242.