Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2017001(2021)

Retinal Vessel Segmentation Method Based on Multi-Scale Attention Analytic Network

Wenjie Luo, Guoqing Han*, and Xuedong Tian
Author Affiliations
  • School of Cyber Security and Computer, Hebei University, Baoding, Hebei 071002, China
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    References(36)

    [2] Niu Y H, Gu L, Lu F et al. Pathological evidence exploration in deep retinal image diagnosis[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 1093-1101(2019).

    [5] Bankhead P, Scholfield C N, McGeown J G et al. Fast retinal vessel detection and measurement using wavelets and edge location refinement[J]. PLoS One, 7, e32435(2012).

    [6] Nguyen U T V, Bhuiyan A, Park L A F et al. An effective retinal blood vessel segmentation method using multi-scale line detection[J]. Pattern Recognition, 46, 703-715(2013).

    [7] Wang W B, Li C B, Zheng C J. Retinal blood vessel segmentation using Hessian based orientational adaptive Gabor wavelet[J]. Laser & Optoelectronics Progress, 57, 081023(2020).

    [8] Soares J V B, Leandro J J G, Cesar R M et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification[J]. IEEE Transactions on Medical Imaging, 25, 1214-1222(2006).

    [9] Lupascu C A, Tegolo D, Trucco E. FABC: retinal vessel segmentation using AdaBoost[J]. IEEE Transactions on Information Technology in Biomedicine, 14, 1267-1274(2010).

    [10] Zhang S, Li Y P. Retinal vascular image segmentation based on improved HED network[J]. Acta Optica Sinica, 40, 0610002(2020).

    [11] Wu C Y, Yi B S, Zhang Y G et al. Retinal vessel image segmentation based on improved convolutional neural network[J]. Acta Optica Sinica, 38, 1111004(2018).

    [12] Wu Y C, Xia Y, Song Y et al. Vessel-net: retinal vessel segmentation under multi-path supervision[M]. //Shen D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019. Lecture notes in computer science, 11764, 264-272(2019).

    [13] Wang B, Qiu S, He H G. Dual encoding U-net for retinal vessel segmentation[M]. //Shen D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019. Lecture notes in computer science, 11764, 84-92(2019).

    [14] 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).

    [17] Ronneberger O, Fischer P, Brox T. U-net:convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

    [18] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

    [20] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6230-6239(2017).

    [21] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 1-9(2015).

    [22] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

    [23] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

    [25] Owen C G, Rudnicka A R, 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]. Investigative Ophthalmology & Visual Science, 50, 2004-2010(2009).

    [26] 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).

    [27] 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, 64, 16-27(2017).

    [28] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile, 1026-1034(2015).

    [29] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 2999-3007(2017).

    [30] 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).

    [31] Guan S, Khan A A, Sikdar S et al. Fully dense U-Net for 2-D sparse photoacoustic tomography artifact removal[J]. IEEE Journal of Biomedical and Health Informatics, 24, 568-576(2020).

    [32] 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, 35, 109-118(2016).

    [34] Yang B, Liu X F, Zhang J. Medical image segmentation based on deep feature aggregation network[J]. Computer Engineering, 47, 187-196(2021).

    [35] Yan Z Q, Yang X, Cheng K T. A three-stage deep learning model for accurate retinal vessel segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 23, 1427-1436(2019).

    [36] Wang X H, Jiang X D, Ren J F. Blood vessel segmentation from fundus image by a cascade classification framework[J]. Pattern Recognition, 88, 331-341(2019).

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    Wenjie Luo, Guoqing Han, Xuedong Tian. Retinal Vessel Segmentation Method Based on Multi-Scale Attention Analytic Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2017001

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    Paper Information

    Category: Medical Optics and Biotechnology

    Received: Dec. 7, 2020

    Accepted: Jan. 11, 2021

    Published Online: Oct. 15, 2021

    The Author Email: Han Guoqing (1655951911@qq.com)

    DOI:10.3788/LOP202158.2017001

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