[1] LI T, BO W, HU C Y, et al. Applications of deep learning in fundus images: a review[J]. Medical Image Analysis, 2021, 69: 101971.
[2] WU Y C, XIA Y, SONG Y, et al. NFN+: a novel network followed network for retinal vessel segmentation[J]. Neural Networks, 2020, 126: 153-162.
[3] LI X, JIANG Y C, LI M L, et al. Lightweight attention convo-lutional neural network for retinal vessel segmentation[J]. IEEE Transactions on Industrial Informatics, 2021, 17(3): 1958-1967.
[4] 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, 2021, 70: 102025.
[5] WANG S J, YU L Q, LI K, et al. DoFE: domain-oriented feature embedding for generalizable fundus image segmenta-tion on unseen datasets[J]. IEEE Transactions on Medical Imaging, 2020, 39(12): 4237-4248.
[6] YAN Z Q, YANG X, CHENG K T. A three-stage deep lear-ning model for accurate retinal vessel segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(4): 1427-1436.
[7] GOLKAR E, RABBANI H, DEHGHANI A. Hybrid registra-tion of retinal fluorescein angiography and optical coherence tomography images of patients with diabetic retinopathy[J]. Biomedical Optics Express, 2021, 12(3): 1707-1724.
[8] WANG D Y, HAYTHAM A, POTTENBURGH J, et al. Hard attention net for automatic retinal vessel segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3384-3396.
[9] RODRIGUES é O, CONCI A, LIATSIS P. ELEMENT: multi-modal retinal vessel segmentation based on a coupled region growing and machine learning approach[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3507-3519.
[10] JIN Q G, MENG Z P, PHAM T D, et al. DUNet: a deformable network for retinal vessel segmentation[J]. Knowledge-Based Systems, 2019, 178: 149-162.
[11] OLIVEIRA A, PEREIRA S, SILVA C A. Retinal vessel seg-mentation based on fully convolutional neural networks[J]. Expert Systems with Applications, 2018, 112: 229-242.
[12] WU Y C, XIA Y, SONG Y, et al. Multiscale network followed network model for retinal vessel segmentation[C]//LNCS 11071: Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Interven-tion, Granada, Sep 16-20, 2018. Cham: Springer, 2018: 119-126.
[13] LIAN S, LI L, LIAN G R, et al. A global and local enhanced residual U-Net for accurate retinal vessel segmentation[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, 18(3): 852-862.
[14] WANG W, ZHONG J F, WU H S, et al. RVSeg-Net: an efficient feature pyramid cascade network for retinal vessel segmentation[C]//LNCS 12265: Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Lima, Oct 4-8, 2020. Cham: Springer, 2020: 796-805.
[15] XU H, ZHU Y H, ZHEN T, et al. Survey of image semantic segmentation methods based on deep neural network[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(1): 47-59.
徐辉, 祝玉华, 甑彤, 等. 深度神经网络图像语义分割方法综述[J]. 计算机科学与探索, 2021, 15(1): 47-59.
[16] VLACHOS M, DERMATAS E. Multi-scale retinal vessel segmentation using line tracking[J]. Computerized Medical Imaging and Graphics, 2010, 34(3): 213-227.
[17] ZHAO Y T, LIU Y H, WU X Q, et al. Retinal vessel segmentation: an efficient graph cut approach with retinex and local phase[J]. PLoS One, 2015, 10(4): e0122332.
[18] FRAZ M M, REMAGNINO P, HOPPE A, et al. An ensemble classification-based approach applied to retinal blood vessel segmentation[J]. IEEE Transactions on Biomedical Enginee-ring, 2012, 59(9): 2538-2548.
[19] LI Q L, FENG B W, XIE L P, et al. A cross-modality learning approach for vessel segmentation in retinal images[J]. IEEE Transactions on Medical Imaging, 2016, 35(1): 109-118.
[20] GU Z W, CHENG J, FU H Z, et al. CE-Net: context encoder network for 2D medical image segmentation[J]. IEEE Tran-sactions on Medical Imaging, 2019, 38(10): 2281-2292.
[21] ZHOU Y K, CHEN Z L, SHEN H L, et al. A refined equili-brium generative adversarial network for retinal vessel seg-mentation[J]. Neurocomputing, 2021, 437: 118-130.
[22] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. arXiv:1406.2661, 2014.
[23] MINAEE S, BOYKOV Y, PORIKLI F, et al. Image segmen-tation using deep learning: a survey[J]. arXiv:2001.05566, 2020.
[24] SON J, PARK S J, JUNG K H. Retinal vessel segmentation in fundoscopic images with generative adversarial networks[J]. arXiv:1706.09318, 2017.
[25] YAN Z Q, YANG X, CHENG K T. Joint segment-level and pixel-wise losses for deep learning based retinal vessel seg-mentation[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(9): 1912-1923.
[26] GUO S, WANG K, KANG H, et al. BTS-DSN: deeply supe-rvised neural network with short connections for retinal vessel segmentation[J]. International Journal of Medical Informatics, 2019, 126: 105-113.
[27] XIAO X, LIAN S, LUO Z M, et al. Weighted Res-UNet for high-quality retina vessel segmentation[C]//Proceedings of the 2018 9th International Conference on Information Tech-nology in Medicine and Education, Hangzhou, Oct 19-21, 2018. Piscataway: IEEE, 2018: 327-331.
[28] ZHANG B H, HUANG S L, HU S H. Multi-scale neural networks for retinal blood vessels segmentation[J]. arXiv:1804.04206, 2018.
[29] XU R, YE X C, JIANG G L, et al. Retinal vessel segmenta-tion via a semantics and multi-scale aggregation network[C]//Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, May 4-8, 2020. Piscataway: IEEE, 2020: 1085-1089.
[30] WANG B, QIU S, HE H G. Dual encoding U-Net for retinal vessel segmentation[C]//LNCS 11764: Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, Oct 13-17, 2019. Cham: Springer, 2019: 84-92.
[31] HAJABDOLLAHI M, ESFANDIARPOOR R, NAJARIAN K, et al. Low complexity convolutional neural network for vessel segmentation in portable retinal diagnostic devices[C]//Proceedings of the 2018 IEEE International Conference on Image Processing, Athens, Oct 7-10, 2018. Piscataway: IEEE, 2018: 2785-2789.
[32] WU Y C, XIA Y, SONG Y, et al. Vessel-Net: retinal vessel segmentation under multi-path supervision[C]//LNCS 11764: Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, Oct 13-17, 2019. Cham: Springer, 2019: 264-272.
[33] CHERUKURI V, KUMAR B G V, BALA R, et al. Multi-scale regularized deep network for retinal vessel segmentation[C]//Proceedings of the 2019 IEEE International Conference on Image Processing, Taipei, China, Sep 22-25, 2019. Pis-cataway: IEEE, 2019: 824-828.
[34] FENG S T, ZHUO Z S, PAN D R, et al. CcNet: a cross-connected convolutional network for segmenting retinal vessels using multi-scale features[J]. Neurocomputing, 2019, 392: 268-276.
[35] SONG J, LEE B. Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks[C]//Proceedings of the 39th Annual Inter-national Conference of the IEEE Engineering in Medicine and Biology Society, Jeju Island, Jul 11-15, 2017. Piscata-way: IEEE, 2017: 681-684.
[36] LONG J, SHELHAMER E, DARRELL T. Fully convolu-tional networks for semantic segmentation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun 7-12, 2015. Washington: IEEE Computer Society, 2015: 3431-3440.
[37] RONNEBERGER O, FISCHER P, BROX T. U-net: convo-lutional networks for biomedical image segmentation[C]//LNCS 9351: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Inter-vention, Munich, Oct 5-9, 2015. Cham: Springer, 2015: 234-241.
[38] MILLETARI F, NAVAB N, AHMADI S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]//Proceedings of the 4th International Con-ference on 3D Vision, Stanford, Oct 25-28, 2016. Washington: IEEE Computer Society, 2016: 565-571.
[39] TIAN C, FANG T, FAN Y L, et al. Multi-path convolutional neural network in fundus segmentation of blood vessels[J]. Biocybernetics and Biomedical Engineering, 2020, 40(2): 583-595.
[40] KHAN T M, ABDULLAH F, NAQVI S S, et al. Shallow vessel segmentation network for automatic retinal vessel segmentation[C]//Proceedings of the 2020 International Joint Conference on Neural Networks, Glasgow, Jul 19-24, 2020. Piscataway: IEEE, 2020: 1-7.
[41] HU K, ZHANG Z Z, NIU X R, et al. Retinal vessel segmen-tation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss func-tion[J]. Neurocomputing, 2018, 309: 179-191.
[42] ALOM M Z, HASAN M, YAKOPCIC C, et al. Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation[J]. arXiv:1802.06955, 2018.
[43] LIU B, GU L, LU F. Unsupervised ensemble strategy for retinal vessel segmentation[C]//LNCS 11764: Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, Oct 13-17, 2019. Cham: Springer, 2019: 111-119.
[44] KHAN T M, NAQVI S S, ARSALAN M, et al. Exploiting residual edge information in deep fully convolutional neural networks for retinal vessel segmentation[C]//Proceedings of the 2020 International Joint Conference on Neural Networks, Glasgow, Jul 19-24, 2020. Piscataway: IEEE, 2020: 1-8.
[45] YANG L, WANG H X, ZENG Q S, et al. A hybrid deep segmentation network for fundus vessels via deep-learning framework[J]. Neurocomputing, 2021, 448: 168-178.
[46] GRIDACH M. PyDiNet: pyramid dilated network for medical image segmentation[J]. Neural Networks, 2021, 140: 274-281.
[47] LAIBACHER T, WEYDE T, JALALI S. M2U-Net: effective and efficient retinal vessel segmentation for resource-con-strained environments[J]. arXiv:1811.07738, 2018.
[48] WEI J, FAN Z. Genetic U-Net: automatically designing light-weight U-shaped CNN architectures using the genetic algo-rithm for retinal vessel segmentation[J]. arXiv:2010.15560, 2020.
[49] ATLI ?, GEDIK O S. Sine-Net: a fully convolutional deep learning architecture for retinal blood vessel segmentation[J]. Engineering Science and Technology, an International Journal, 2021, 24(2): 271-283.
[50] SAMUEL P M, VEERAMALAI T. VSSC Net: vessel specific skip chain convolutional network for blood vessel segmenta-tion[J]. Computer Methods and Programs in Biomedicine, 2021, 198: 105769.
[51] LIANG J J, WEI J J, JIANG Z F. Generative adversarial networks GAN overview[J]. Journal of Frontiers of Com-puter Science and Technology, 2020, 14(1): 1-17.
梁俊杰, 韦舰晶, 蒋正锋. 生成对抗网络GAN综述[J]. 计算机科学与探索, 2020, 14(1): 1-17. |