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Volume 38 Issue 3
May  2023
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Wang HD, Li ZZ, Okuwobi IP et al. PCRTAM-Net: A novel pre-activated convolution residual and triple attention mechanism network for retinal vessel segmentation. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(3): 567−581 May 2023. DOI: 10.1007/s11390-023-3066-4.
Citation: Wang HD, Li ZZ, Okuwobi IP et al. PCRTAM-Net: A novel pre-activated convolution residual and triple attention mechanism network for retinal vessel segmentation. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(3): 567−581 May 2023. DOI: 10.1007/s11390-023-3066-4.
shu

PCRTAM-Net: A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

  • 1.

    Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin 541004, China

  • 2.

    School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China

  • 3.

    School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China

  • 4.

    Department of Pathology, Ganzhou Municipal Hospital, Ganzhou 341000, China

Funds: This work was partially supported by the Open Funds from Guangxi Key Laboratory of Image and Graphic Intelligent Processing under Grant No. GIIP2209, the National Natural Science Foundation of China under Grant Nos. 62172120 and 62002082, and the Guangxi Natural Science Foundation of China under Grant Nos. 2019GXNSFAA245014 and 2020GXNSFBA238014.
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  • Author Bio:

    Hua-Deng Wang received his B.S. and M.S. degrees in computer science and technology from Huazhong University of Science and Technology, Wuhan, in 2002 and 2007 respectively, and his Ph.D. degree in information and communication engineering from Guilin University of Electronic Technology, Guilin, in 2022. He is currently a professor at the School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin. His current research interests include medical image processing

    Zi-Zheng Li received his B.S. degree in electronic information engineering from Dezhou University, Dezhou, in 2020. He is currently a Master student with the School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin. His current research interests include medical image segmentation

    Idowu Paul Okuwobi received his Ph.D. degree in computer science and technology from Nanjing University of Science and Technology, Nanjing, in 2019. He is currently an associate professor with the School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin. He oversees the Vision and Image Processing Lab, Guilin University of Electronic Technology, where his current research objective is to develop new intelligent algorithms for medical diagnosis and treatment

    Bing-Bing Li received his B.S. degree in clinical medicine from Gannan Medical University, Ganzhou, in 2018, and his M.S. degree in clinical medicine from Southern Medical University, Guangzhou, in 2021. Since then, he has worked in the Department of Pathology, Ganzhou Municipal Hospital, Ganzhou, and served as the deputy director of the Department of Pathology. His current research interests include tumor pathology, molecular pathology, and computational pathology

    Xi-Peng Pan received his Ph.D. degree in control science and engineering from the Beijing University of Posts and Telecommunications, Beijing, in 2019. He is a Master supervisor in the School of Computer Science and Information Security of Guilin University of Electronic Technology, Guilin. His research interests include machine learning

    Zhen-Bing Liu received his Ph.D. degree in pattern recognition and intelligent system from Huazhong University of Science and Technology, Wuhan, in 2010. He is currently a professor at the School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin. His main research interests include image processing

    Ru-Shi Lan received his Ph.D. degree in soft engineering from the University of Macau, Macau, in 2016. He is currently an associate professor with the School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin. His research interests include image processing. He received the Guangxi Science Fund for Distinguished Young Scholars granted by the Science and Technology Department of Guangxi Zhuang Autonomous Region

    Xiao-Nan Luo received his Ph.D. degree in computational mathematics from the Dalian University of Technology, Dalian, in 1991. From 1995 to 2016, Dr. Luo was a professor at the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou. He is currently a professor at the School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin. His research interests include computer graphics and CAD. He received the National Science Fund for Distinguished Young Scholars granted by the National Natural Science Foundation of China

  • Corresponding author:

    (Idowu Paul Okuwobi worked on the investigation and writing and provided computing power, and Bing-Bing Li was responsible for the data curation and methodology.)

    libingbing19932021@126.com

  • Received Date: December 29, 2022
  • Accepted Date: May 21, 2023
    Abstract

  • Retinal images play an essential role in the early diagnosis of ophthalmic diseases. Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background. At the same time, automated models struggle to capture representative and discriminative retinal vascular features. To fully utilize the structural information of the retinal blood vessels, we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network (PCRTAM-Net). PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network. In addition, the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow. A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies. We evaluate the proposed PCRTAM-Net on four publicly available datasets, DRIVE, CHASE_DB1, STARE, and HRF. Our model achieves state-of-the-art performance of 97.10%, 97.70%, 97.68%, and 97.14% for ACC and 83.05%, 82.26%, 84.64%, and 81.16% for F1, respectively.

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    • References (37)
  • [1]
    Li L Z, Verma M, Nakashima Y, Nagahara H, Kawasaki R. IterNet: Retinal image segmentation utilizing structural redundancy in vessel networks. In Proc. the 2020 IEEE Winter Conference on Applications of Computer Vision, Mar. 2020, pp.3645-3654. DOI: 10.1109/WACV45572.2020.9093621.
    [2]
    Li Z Y, Zhang X F, Muller H, Zhang S T. Large-scale retrieval for medical image analytics: A comprehensive review. Medical Image Analysis, 2018, 43: 66–84. DOI: 10.1016/j.media.2017.09.007.
    [3]
    Huang K, Yan M. A region based algorithm for vessel detection in retinal images. In Proc. the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct. 2006, pp.645-653. DOI: 10.1007/11866565_79.
    [4]
    Hoover A D, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Medical Imaging, 2002, 19(3): 203–210. DOI: 10.1109/42.845178.
    [5]
    Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Medical Imaging, 2016, 35(11): 2369–2380. DOI: 10.1109/TMI.2016.2546227.
    [6]
    Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In Proc. the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct. 2015, pp.234-241. DOI: 10.1007/978-3-319-24574-4_28.
    [7]
    Staal J, Abramoff M D, Niemeijer M, Viergever M A, Van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Medical Imaging, 2004, 23(4): 501–509. DOI: 10.1109/TMI.2004.825627.
    [8]
    Orlando J I, Prokofyeva E, Blaschko M B. A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomedical Engineering, 2017, 64(1): 16–27. DOI: 10.1109/TBME.2016.2535311.
    [9]
    Sheng B, Li P, Mo S J, Li H T, Hou X H, Wu Q, Qin J, Fang R G, Feng D D. Retinal vessel segmentation using minimum spanning superpixel tree detector. IEEE Trans. Cybernetics, 2018, 49(7): 2707–2719. DOI: 10.1109/TCYB.2018.2833963.
    [10]
    Yin B J, Li H T, Sheng B, Hou X H, Chen Y, Wu W, Li P, Shen R M, Bao Y Q, Jia W P. Vessel extraction from non-fluorescein fundus images using orientation-aware detector. Medical Image Analysis, 2015, 26(1): 232–242. DOI: 10.1016/j.media.2015.09.002.
    [11]
    Dai L, Wu L, Li H T, Cai C, Wu Q, Kong H Y, Liu R H, Wang X N, Hou X H, Liu Y X, Long X X, Wen Y, Lu L N, Shen Y X, Chen Y, Shen D G, Yang X K, Zou H D, Sheng B, Jia W P. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications, 2021, 12: Article No. 3242. DOI: 10.1038/s41467-021-23458-5.
    [12]
    Wang D Y, Haytham A, Pottenburgh J, Saeedi O, Tao Y. Hard attention net for automatic retinal vessel segmentation. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3384–3396. DOI: 10.1109/JBHI.2020.3002985.
    [13]
    Sun M Y, Li K Q, Qi X Q, Dang H, Zhang G H. Contextual information enhanced convolutional neural networks for retinal vessel segmentation in color fundus images. Journal of Visual Communication and Image Representation, 2021, 77: 103134. DOI: 10.1016/j.jvcir.2021.103134.
    [14]
    Jin Q G, Meng Z P, Pham T D, Chen Q, Wei L Y, Su R. DUNet: A deformable network for retinal vessel segmentation. Knowledge-Based Systems, 2019, 178: 149–162. DOI: 10.1016/j.knosys.2019.04.025.
    [15]
    Mou L, Chen L, Cheng J, Gu Z W, Zhao Y T, Liu J. Dense dilated network with probability regularized walk for vessel detection. IEEE Trans. Medical Imaging, 2020, 39(5): 1392–1403. DOI: 10.1109/TMI.2019.2950051.
    [16]
    Wei J H, Zhu G J, Fan Z, Liu J C, Rong Y B, Mo J J, Li W J, Chen X J. Genetic U-Net: Automatically designed deep networks for retinal vessel segmentation using a genetic algorithm. IEEE Trans. Medical Imaging, 2022, 41(2): 292–307. DOI: 10.1109/TMI.2021.3111679.
    [17]
    Fu J, Liu J, Tian H J, Li Y, Bao Y J, Fang Z W, Lu H Q. Dual attention network for scene segmentation. In Proc. the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2019, pp.3141-3149. DOI: 10.1109/CVPR.2019.00326.
    [18]
    Yang Q, Ma B Q, Cui H, Ma J Q. AMF-NET: Attention-aware multi-scale fusion network for retinal vessel segmentation. In Proc. the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Nov. 2021, pp.3277-3280. DOI: 10.1109/EMBC46164.2021.9630756.
    [19]
    Wu T F, Li L L, Li J B. MSCAN: Multi-scale channel attention for fundus retinal vessel segmentation. In Proc. the 2nd IEEE International Conference on Power Data Science, Dec. 2020, pp.18-27. DOI: 10.1109/ICPDS51559.2020.9332494.
    [20]
    Wu H S, Wang W, Zhong J F, Lei B Y, Wen Z K, Qin J. SCS-Net: A scale and context sensitive network for retinal vessel segmentation. Medical Image Analysis, 2021, 70: 102025. DOI: 10.1016/j.media.2021.102025.
    [21]
    Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In Proc. the 14th International Conference on Artificial Intelligence and Statistics, Apr. 2011, pp.315-323.
    [22]
    Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Jul. 2017, pp.2261-2269. DOI: 10.1109/CVPR.2017.243.
    [23]
    He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp.770-778. DOI: 10.1109/CVPR.2016.90.
    [24]
    Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. the 32nd International Conference on Machine Learning, Jul. 2015, pp.448-456.
    [25]
    Mou L, Zhao Y T, Fu H Z, Liu Y H, Cheng J, Zheng Y L, Su P, Yang J L, Chen L, Frangi A F, Akiba M, Liu J. CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging. Medical Image Analysis, 2021, 67: 101874. DOI: 10.1016/j.media.2020.101874.
    [26]
    Shi Z J, Wang T Y, Huang Z, Xie F, Liu Z H, Wang B L, Xu J. MD-Net: A multi-scale dense network for retinal vessel segmentation. Biomedical Signal Processing and Control, 2021, 70: 102977. DOI: 10.1016/j.bspc.2021.102977.
    [27]
    Owen C G, Rudnicka A R, Mullen R, Barman S A, Monekosso D, Whincup P H, Ng J, Paterson C. Measuring retinal vessel tortuosity in 10-year-old children: Validation of the computer-assisted image analysis of the retina (CAIAR) program. Investigative Ophthalmology & Visual Science, 2009, 50(5): 2004–2010. DOI: 10.1167/iovs.08-3018.
    [28]
    Köhler T, Budai A, Kraus M F, Odstrčilik J, Michelson G, Hornegger J. Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In Proc. the 26th IEEE International Symposium on Computer-Based Medical Systems, Jun. 2013, pp.95-100. DOI: 10.1109/CBMS.2013.6627771.
    [29]
    Woo S, Park J, Lee J Y, Kweon I S. CBAM: Convolutional block attention module. In Proc. the 15th European Conference on Computer Vision, Sept. 2018, pp.3-19. DOI: 10.1007/978-3-030-01234-2_1.
    [30]
    Zhuo Z S, Huang J P, Lu K, Pan D R, Feng S T. A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index. Computer Methods and Programs in Biomedicine, 2020, 196: 105508. DOI: 10.1016/j.cmpb.2020.105508.
    [31]
    Wu Y C, Xia Y, Song Y, Zhang Y N, Cai W D. NFN+: A novel network followed network for retinal vessel segmentation. Neural Networks, 2020, 126: 153–162. DOI: 10.1016/j.neunet.2020.02.018.
    [32]
    Khan T M, Khan M A U, Rehman N U, Naveed K, Afridi I U, Naqvi S S, Raazak I. Width-wise vessel bifurcation for improved retinal vessel segmentation. Biomedical Signal Processing and Control, 2022, 71: 103169. DOI: 10.1016/j.bspc.2021.103169.
    [33]
    Li Q L, Feng B W, Xie L P, Liang P, Zhang H S, Wang T F. A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Medical Imaging, 2016, 35(1): 109–118. DOI: 10.1109/TMI.2015.2457891.
    [34]
    Wu Y C, Xia Y, Song Y, Zhang Y N, Cai W D. Multiscale network followed network model for retinal vessel segmentation. In Proc. the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, Sept. 2018, pp.119-126.
    [35]
    Tang P, Liang Q K, Yan X T, Zhang D, Coppola G, Sun W. Multi-proportion channel ensemble model for retinal vessel segmentation. Computers in Biology and Medicine, 2019, 111: 103352. DOI: 10.1016/j.compbiomed.2019.103352.
    [36]
    Dong F F, Wu D Y, Guo C Y, Zhang S T, Yang B L, Gong X Y. CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation. Computers in Biology and Medicine, 2022, 147: 105651. DOI: 10.1016/j.compbiomed.2022.105651.
    [37]
    Soomro T A, Afifi A J, Gao J B, Hellwich O, Zheng L H, Paul M. Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Systems with Applications, 2019, 134: 36–52. DOI: 10.1016/j.eswa.2019.05.029.
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