Chinese Journal of Liquid Crystals and Displays, Volume. 36, Issue 12, 1702(2021)

Retinal image segmentation method based on dense cycle networks

YANG Yun1, ZHOU Shu-jie2, LI Cheng-hui2, and ZHANG Juan-juan2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    References(22)

    [1] [1] YIN Y, ADEL M, BOURENNANE S. Retinal vessel segmentation using a probabilistic tracking method [J]. Pattern Recognition, 2012, 45(4): 1235-1244.

    [2] [2] YANG Y, HUANG S Y, RAO N N. An automatic hybrid method for retinal blood vessel extraction [J]. International Journal of Applied Mathematics and Computer Science, 2008, 18(3): 399-407.

    [3] [3] LI Q, YOU J, ZHANG D. Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses [J]. Expert Systems with Applications, 2012, 39(9): 7600-7610.

    [4] [4] OLIVEIRA A, PEREIRA S, SILVA C A. Retinal vessel segmentation based on fully convolutional neural networks [J]. Expert Systems with Application, 2018, 112: 229-242.

    [5] [5] GUO C L, SZEMENYEI M, PEI Y, et al. SD-Unet: a structured dropout U-Net for retinal vessel segmentation [C]//2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). Athens: IEEE, 2019: 439-444.

    [6] [6] ROY A G, CONJETI S, KARRI S P K, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks [J]. Biomedical Optics Express, 2017, 8(8): 3627-3642.

    [7] [7] 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, 2019, 38(10): 2281-2292.

    [8] [8] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks [J]. Advances in Neural Information Processing Systems, 2014, 3(11): 2672-2680.

    [9] [9] CHEN Y Y, JIN W Y, WANG M, et al. Metallographic image segmentation of GCr15 bearing steel based on CGAN [J]. International Journal of Applied Electromagnetics and Mechanics, 2020, 64(1/4): 1237-1243.

    [10] [10] LI M Y, TANG H L, CHAN M D, et al. DC-AL GAN: pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet [J]. Medical Physics, 2020, 47(3): 1139-1150.

    [11] [11] KADAMBI S, WANG Z Y, XING E. WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images [J]. International Journal of Computer Assisted Radiology and Surgery, 2020, 15(7): 1205-1213.

    [12] [12] ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks [C]//Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2242-2251.

    [13] [13] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation [C]//Proceeding of the 18th International Conference on Medical Image Computing and Computer-assisted Intervention (MICCAI). Munich: Springer, 2015: 234-241.

    [14] [14] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.

    [17] [17] LAHIRI A, ROY A G, SHEET D, et al. Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography [C]//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando: IEEE, 2016: 1340-1343.

    [18] [18] VEGA R, SANCHEZ-ANTE G, FALCON-MORALES L E, et al. Retinal vessel extraction using lattice neural networks with dendritic processing [J]. Computers in Biology and Medicine, 2015, 58: 20-30.

    [19] [19] 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 preprint arXiv: 1802.06955, 2018.

    [20] [20] 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.

    [21] [21] ORLANDO J I, PROKOFYEVA E, BLASCHKO M B. A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images [J]. IEEE Transactions on Biomedical Engineering, 2017, 64(1): 16-27.

    [22] [22] ZHUANG J. LadderNet: Multi-path networks based on U-Net for medical image segmentation [J]. arXiv preprint arXiv: 1810.07810, 2018.

    [23] [23] 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, 2018, 65(9): 1912-1923.

    Tools

    Get Citation

    Copy Citation Text

    YANG Yun, ZHOU Shu-jie, LI Cheng-hui, ZHANG Juan-juan. Retinal image segmentation method based on dense cycle networks[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(12): 1702

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: May. 22, 2021

    Accepted: --

    Published Online: Jan. 1, 2022

    The Author Email:

    DOI:10.37188/cjlcd.2021-0142

    Topics