Skip to main content
SpringerLink
Log in

DU-Net: A Novel Architecture for Retinal Vessels Segmentation

  • Conference paper
  • First Online:
Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13657))

Included in the following conference series:

  • 577 Accesses

Abstract

Early diagnosis is fundamental for ophthalmic diseases which may cause deterioration of the human vision system, such as hypertension, glaucoma, and diabetic retinopathy. Ophthalmologists usually examine fundus images to evaluate the clinical condition of retinal blood vessels, which becomes a significant indicator for diagnosing various ophthalmic diseases. Whereas, manually labeling retinal vessels is a time-consuming and burdensome task, and it is also required extensive clinical experience. Therefore, it is necessary to implement automatic segmentation for retinal vessels. This paper integrates the dense-block into a U-Net to propose a novel network structure called DU-net, which can improve the accuracy of blood vessel segmentation by alleviating the problems of gradient disappearance and image structure feature loss. Related experiments are conducted on one publicly available fundus image dataset (DRIVE). The results demonstrate that the proposed DU-net outperforms most comparison methods in terms of different evaluation metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pathan, S., Kumar, P., Pai, R.M., et al.: Automated segmentation and classifcation of retinal features for glaucoma diagnosis. Biomed. Signal Process. Control 63, 102244 (2021)

    Article  Google Scholar 

  2. Jin, Q., Meng, Z., Pham, T.D., et al.: DUNet: a deformable network for retinal vessel segmentation. Knowl.-Based Syst. 178, 149–162 (2019)

    Article  Google Scholar 

  3. Salamat, N., Missen, M.M.S., Rashid, A.: Diabetic retinopathy techniques in retinal images: a review. Artif. Intell. Med. 97, 168–188 (2019)

    Article  Google Scholar 

  4. Badar, M., Haris, M., Fatima, A.: Application of deep learning for retinal image analysis: a review. Comput. Sci. Rev. 35, 100203 (2020)

    Article  MathSciNet  Google Scholar 

  5. Wang, S., Ouyang, X., Liuang, T., et al.: Follow my eye: using gaze to supervise computer-aided diagnosis. IEEE Trans. Med. Imaging (2022). https://doi.org/10.1109/TMI.2022.3146973

    Article  Google Scholar 

  6. Chaudhuri, S., Chatterjee, S., Katz, N., et al.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)

    Article  Google Scholar 

  7. Rodrigues, L.C., Marengoni, M.: Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomed. Signal Process. Control 36, 39–49 (2017)

    Article  Google Scholar 

  8. Imani, E., Javidi, M., Pourreza, H.R.: Improvement of retinal blood vessel detection using morphological component analysis. Comput. Methods Programs Biomed. 118(3), 263–279 (2015)

    Article  Google Scholar 

  9. Singh, N.P., Srivastava, R.: Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. Comput. Methods Programs Biomed. 129, 40–50 (2016)

    Article  Google Scholar 

  10. Ramani, G., Menakadevi, T.: Detection of diabetic retinopathy using discrete wavelet transform with discrete Meyer in retinal images. J. Med. Imaging Health Infor. 12(1), 62–67 (2022)

    Article  Google Scholar 

  11. Ramos-Soto, O., Rodriguez-Esparza, E., Balderas-Mata, S.E., et al.: An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering. Comput. Methods Programs Biomed. 201, 105949 (2021)

    Article  Google Scholar 

  12. Haq, I.U., Nagoaka, R., Makino, T., et al.: 3D Gabor wavelet based vessel filtering of photoacoustic images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3883–3886. IEEE (2016)

    Google Scholar 

  13. Zhao, Y., Xie, J., Pan, S., Zheng, Y., Liu, Y., Cheng, J., Liu, J.: Retinal artery and vein classification via dominant sets clustering-based vascular topology estimation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 56–64. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_7

    Chapter  Google Scholar 

  14. Balasubramanian, K., Ananthamoorthy, N.P.: Robust retinal blood vessel segmentation using convolutional neural network and support vector machine. J. Ambient. Intell. Humaniz. Comput. 12(3), 3559–3569 (2019). https://doi.org/10.1007/s12652-019-01559-w

    Article  Google Scholar 

  15. Preethy Rebecca, P., Allwin, S.: Detection of DR from retinal fundus images using prediction ANN classifier and RG based threshold segmentation for diabetes. J. Ambient. Intell. Humaniz. Comput. 12(12), 10733–10740 (2021). https://doi.org/10.1007/s12652-020-02882-3

    Article  Google Scholar 

  16. Staal, J., Abramoff, M.D., Niemeijer, M., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–5091 (2004)

    Article  Google Scholar 

  17. 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. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)

    Article  Google Scholar 

  18. Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)

    Article  Google Scholar 

  19. Fraz, M.M., Remagnino, P., Hoppe, A., et al.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538–2548 (2012)

    Article  Google Scholar 

  20. Gu, J., Wang, Z., Kuen, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)

    Article  Google Scholar 

  21. Zhao, Z.Q., Zheng, P., Xu, S., et al.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)

    Article  Google Scholar 

  22. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)

    Google Scholar 

  23. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  24. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  25. Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)

  26. Larsson, G., Maire, M., Shakhnarovich, G.: Fractalnet: Ultra-deep neural networks without residuals. arXiv preprint arXiv:1605.07648 (2016)

  27. Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  28. Rahman, S., Rahman, M.M., Abdullah-Al-Wadud, M., Al-Quaderi, G.D., Shoyaib, M.: An adaptive gamma correction for image enhancement. EURASIP J. Image Video Process. 2016(1), 1–13 (2016). https://doi.org/10.1186/s13640-016-0138-1

    Article  Google Scholar 

  29. Strisciuglio, N., Azzopardi, G., Vento, M., et al.: Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Mach. Vis. Appl. 27(8), 1137–1149 (2016)

    Article  Google Scholar 

  30. Gao, X., Cai, Y., Qiu, C., et al.: Retinal blood vessel segmentation based on the Gaussian matched filter and U-net. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. IEEE, pp. 1–5 (2017)

    Google Scholar 

  31. Wang, W.H., Zhang, J.Z., Wu, W.Y.: Improved morphology combined with Otsu for retinal vessel segmentation. Comput. Appl. Res. 07, 2228–2231 (2019)

    Google Scholar 

  32. Cai, Z.Z., Tang, P., Hu, J.B., et al.: Segmentation of retinal vessels based on PST and multiscale Gaussian filtering. Appl. Res. Comput. 36(06), 1893–1896 (2019)

    Google Scholar 

  33. Alom, M.Z., Hasan, M., Yakopcic, C., et al.: Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955 (2018)

  34. Yan, Z., Yang, X., Cheng, K.T.: Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans. Biomed. Eng. 65(9), 1912–1923 (2018)

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported in part by grants from the National Natural Science Foundation of China (No. 62062040), the Outstanding Youth Project of Jiangxi Natural Science Foundation (No. 20212ACB212003), the Jiangxi Province Key Subject Academic and Technical Leader Funding Project (No. 20212BCJ23017).

Author information

Authors and Affiliations

  1. School of Software, Jiangxi Normal University, Nanchang, China

    Yan Jiang, Lingxia Chen, Jiyong Hu & Ping Li

  2. School of Management and Engineering, Capital University of Economics and Business, Beijing, China

    Ziji Zeng

Authors
  1. Yan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziji Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lingxia Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiyong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ping Li .

Editor information

Editors and Affiliations

  1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA

    Yuan Xu

  2. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Huang Teng

  4. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  5. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Jin Li

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, Y., Zeng, Z., Chen, L., Hu, J., Li, P. (2023). DU-Net: A Novel Architecture for Retinal Vessels Segmentation. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20102-8_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20101-1

  • Online ISBN: 978-3-031-20102-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation