Optical Technique, Volume. 49, Issue 4, 487(2023)

Intra- and inter-scale augmented U-Net for retinal vessel segmentation

YANG Ying1, YUE Shengbin2, CHU Bowen2, and QUAN Haiyan1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    References(39)

    [1] [1] Brand CS. Management of retinal vascular diseases: a patient-centric approach[J]. Eye,2012,26(2):S1-S16.

    [2] [2] Cao L, Li H, Zhang Y, et al. Hierarchical method for cataract grading based on retinal images using improved Haar wavelet[J]. Information Fusion,2020,53:196-208.

    [3] [3] Heneghan C, Flynn J, O’Keefe M, et al. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis[J]. Medical Image Analysis,2002,6(4):407-429.

    [4] [4] Teng T, Lefley M, Claremont D. Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy[J]. Medical and Biological Engineering and Computing,2002,40:2-13.

    [5] [5] Yeung M, Sala E, Schnlieb CB, et al. Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy[J]. Computers in Biology and Medicine,2021,137:104815-104815.

    [6] [6] Zhao X, Zhang L, Lu H. Automatic polyp segmentation via multi-scale subtraction network[C]∥Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention-MICCAI 2021.Strasbourg,France:Springer,2021:120-130.

    [7] [7] Tian F, Li Y, Wang J, et al. Blood vessel segmentation of fundus retinal images based on improved frangi and mathematical morphology[J]. Computational and Mathematical Methods in Medicine,2021,2021:1-11.

    [8] [8] K. Mardani KM. Enhancing retinal blood vessel segmentation in medical images using combined segmentation modes extracted by dbscan and morphological reconstruction[J]. Biomedical Signal Process Control,2021,69:102837.

    [9] [9] Guo C, Szemenyei M, Hu Y, et al. Channel attention residual u-net for retinal vessel segmentation[C]∥ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto:IEEE,2021:1185-1189.

    [10] [10] Lv Y, Ma H, Li J, et al. Attention Guided U-Net with Atrous Convolution for Accurate Retinal Vessels Segmentation[J]. IEEE Access,2020,8:32826-32839.

    [11] [11] Shin H C, Roth H R, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J]. IEEE Transactions on Medical Imaging,2016,35(5):1285-1298.

    [12] [12] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]∥Proceedings of the IEEE conference on computer vision and pattern recognition. Boston:IEEE,2015:3431-3440.

    [13] [13] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]∥ Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention-MICCAI 2015. Munich, Germany: Springer,2015:234-241.

    [14] [14] Yan Z, Yang X, Cheng K-T. A three-stage deep learning model for accurate retinal vessel segmentation[J]. IEEE Transactions on Biomedical Engineering,2018,23(4):1427-1436.

    [15] [15] Wu Y, Xia Y, Song Y, et al. Vessel-Net: Retinal vessel segmentation under multi-path supervision[C]∥ Proceedings of the international conference on Medical Image Computing and Computer Assisted Intervention- MICCAI 2019. Shenzhen, China:Springer,2019:264-272.

    [16] [16] Gu Z, Cheng J, Fu H, et al. CE-Net: context encoder network for 2d medical image segmentation[J]. IEEE Transactions on Medical Imaging,2019,38(10):2281-2292.

    [17] [17] Deng X, Ye J. A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network [J]. Biomedical Signal Processing Control,2022,73:103467.

    [18] [18] Jin Q, Meng Z, Pham TD, et al. DUNet: A deformable network for retinal vessel segmentation[J]. Knowledge-Based Systems,2019,178:149-162.

    [19] [19] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥Proceedings of the IEEE conference on computer vision and pattern recognition,2016:770-778.

    [20] [20] Alom M Z, Hasan M, Yakopcic C, et al. Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net)[C]∥Proceedings of the NAECON 2018-IEEE National Aerospace and Electronics Conference,2018,228-233.

    [21] [21] Zhuang J. LadderNet: Multi-path networks based on U-Net for medical image segmentation[J]. arXiv preprint,2018,arXiv:1810-07810.

    [22] [22] Li X, Jiang Y, Li M, et al. Lightweight attention convolutional neural network for retinal vessel image segmentation[J]. IEEE Transactions on Industrial Informatics,2020,17(3):1958-1967.

    [23] [23] Wang C, Zhao Z, Yu Y. Fine retinal vessel segmentation by combining Nest U-net and patch-learning[J]. Soft Computin,2021,25(7):5519-5532.

    [24] [24] Anita D, Erwin, Bambang S, et al. VG-DropDNet a robust architecture for blood vessels segmentation on retinal image[J]. IEEE Access,2022,10:92067-92083.

    [25] [25] Francia GA, Pedraza C, Aceves M, et al. Chaining a U-net with a residual U-net for retinal blood vessels segmentation[J]. IEEE Access,2020,8:38493-38500.

    [26] [26] Huang Z, Sun M, Liu Y, et al. CSAUNet: A cascade self-attention u-shaped network for precise fundus vessel segmentation[J]. Biomedical Signal Processing and Control,2022,75:103613.

    [27] [27] Wang Z, Zou N, Shen D, et al. Non-local u-nets for biomedical image segmentation[C]∥Proceedings of the AAAI conference on artificial intelligence,New York:AAAI,2020,34(04):6315-6322.

    [28] [28] Bello I, Zoph B, Le Q, et al. Attention augmented convolutional networks[C]∥Proceedings of the IEEE International Conference on Computer Vision. Seoul:IEEE,2019:3285-3294.

    [29] [29] Alom MZ, Yakopcic C, Hasan M, et al. Recurrent residual U-Net for medical image segmentation[J]. Journal of Medical Imaging,2019,6(1):14006.

    [30] [30] Li D, Dharmawan DA, Ng BP, et al. Residual u-net for retinal vessel segmentation[C]∥Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP). Taipei:IEEE,2019:1425-9142.

    [31] [31] Staal J, Abràmoff MD, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina[J]. IEEE Transactions on Medical Imaging,2004,23(4):501-509.

    [32] [32] Hoover AD, 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,2000,19(3):203-210.

    [33] [33] Owen CG, Rudnicka AR, Mullen R, et al. Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program[J]. Invest Ophthalmol Vis Sci,2009,50(5):2004-2010.

    [34] [34] Kingma DP, Ba J. Adam: A method for stochastic optimization [J]. ArXiv Preprint,2014,ArXiv:1412-6980.

    [35] [35] Fan Z, Lu J, Wei C, et al. A hierarchical image matting model for blood vessel segmentation in fundus images[J]. IEEE Transactions on Image Processing,2018,28(5):2367-2377.

    [36] [36] Tang S, Yu F. Construction and verification of retinal vessel segmentation algorithm for color fundus image under BP neural network model[J]. The Journal of Supercomputing,2021,77(4):3870-3884.

    [37] [37] Dong FF, Wu DY, Guo CV, et al. CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation[J]. Computers in Biology and Medicine,2022,147:105651.

    [38] [38] Zhang T, Li J, Zhao Y, et al. MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation[J]. Computers in Biology and Medicine,2022,2022:1-10.

    [39] [39] Liu YH, Shen J, Yang L, et al. ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images [J].Biomedical Signal Processing and Control,2022,79:104087.

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    YANG Ying, YUE Shengbin, CHU Bowen, QUAN Haiyan. Intra- and inter-scale augmented U-Net for retinal vessel segmentation[J]. Optical Technique, 2023, 49(4): 487

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    Received: Nov. 14, 2022

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    Published Online: Jan. 4, 2024

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