Skip to main content
SpringerLink
Log in

RADCU-Net: residual attention and dual-supervision cascaded U-Net for retinal blood vessel segmentation

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The automated segmentation of retinal blood vessels plays an important role in the computer aided diagnosis of retinal diseases. In this study, we propose a novel retinal vessel segmentation method based on residual attention and dual-supervision cascaded U-Net (RADCU-Net). Specifically, a residual attention U-Net (RAU-Net), including a residual unit and an attention mechanism, is constructed to improve the feature representation ability by explicitly modelling the interdependency among the channels of the convolutional features. To boost the accuracy of retinal blood vessel segmentation, a cascaded RAU-Net framework is constructed by concatenating two RAU-Nets with the proposed residual attention modules. Moreover, a dual-supervision training strategy is designed to improve the supervision of the cascaded RAU-Net parameter learning by adding an additional balanced cross-entropy loss function in the middle of the cascaded RAU-Net. The results of extensive experiments on the DRIVE and STARE datasets demonstrate that the proposed method achieves better performance compared to state-of-the-art methods. Our method provides a meaningful attempt to improve blood vessel segmentation and can further facilitate the diagnosis of ophthalmological diseases.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability statement

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Mookiah MRK, Hogg S, MacGillivray TJ, Prathiba V, Pradeepa R, Mohan V, Anjana RM, Doney AS, Palmer CNA, Trucco E (2020) A Review of machine learning methods for retinal blood vessel segmentation and artery/vein classification. Med Image Anal 68:101905

    Article  Google Scholar 

  2. Boudegga H, Elloumi Y, Akil M, Bedoui MH, Kachouri R, Abdallah AB (2021) Fast and efficient retinal blood vessel segmentation method based on deep learning network. Comput Med Imaging Graph 90:101902

    Article  Google Scholar 

  3. Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging 38(10):2281–2292

    Article  Google Scholar 

  4. Sheng B, Li P, Mo S, Li H, Hou X, Wu Q, Qin J, Fang R, Feng DD (2018) Retinal vessel segmentation using minimum spanning superpixel tree detector. IEEE Trans Cybern 49(7):2707–2719

    Article  Google Scholar 

  5. Wang X, Jiang X, Ren J (2019) Blood vessel segmentation from fundus image by a cascade classification framework. Pattern Recognit 88:331–341

    Article  Google Scholar 

  6. Fu H, Xu Y, Wong DWK, Liu J (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: IEEE Int. Symp. Biomed. Imaging, pp 698–701

  7. Wang X, Ju L, Zhao X, Ge Z (2019) Retinal abnormalities recognition using regional multitask learning. In: Int. Conf. Med. Image. Comput. Comput-Assist. Intervention, pp 30–38

  8. Islam M, Vibashan VS, Jose VJM, Wijethilake N, Utkarsh U, Ren H (2019) Brain tumor segmentation and survival prediction using 3D attention UNet. In: Proceedings of International MICCAI Brainlesion Workshop, pp 262–272

  9. Li W, Qin S, Li F, Wang L (2021) MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images. Med Phys 48(1):329–341

    Article  Google Scholar 

  10. Duan W, Chen Y, Zhang Q, Lin X, Yang X (2021) Refined tooth and pulp segmentation using U-Net in CBCT image. Dentomaxillofac Radiol 50(6):20200251

    Article  Google Scholar 

  11. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE Conf. Comput. Vision. Pattern. Recognit., pp. 11531–11539

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

    Article  Google Scholar 

  13. Liu I, Sun Y (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans Med Imaging 12:334–341

    Article  Google Scholar 

  14. Zana F, Klein JC (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10:1010–1019

    Article  MATH  Google Scholar 

  15. Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, Rudnicka AR, Owen CG (2012) An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed 108:600–616

    Article  Google Scholar 

  16. Hoover AD, Kouznetsova VL, Goldbaum MH (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19:203–210

    Article  Google Scholar 

  17. Khan BK, Amir AK, Muhammad S, Wang Y (2016) A morphological Hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding. PLoS ONE 11:e0158996

    Article  Google Scholar 

  18. Liu B, Gu L, Lu F (2019) Unsupervised Ensemble Strategy for Retinal Vessel Segmentation. medical image computing and computer-assisted intervention. In: Int. Conf. Med. Image. Comput. Comput-Assist. Intervention., 2019, pp 111–119

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

    Article  Google Scholar 

  20. Soares JVB, Leandro JJG, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 25:1214–1222

    Article  Google Scholar 

  21. Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59:2538–3548

    Article  Google Scholar 

  22. Staal J, Abramoff MD, Niemeijer M, Viergever MA, Ginneken BV (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509

    Article  Google Scholar 

  23. Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35:2369–2380

    Article  Google Scholar 

  24. Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T (2016) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35:109–118

    Article  Google Scholar 

  25. Yan Z, Xin Y, Cheng KT (2018) Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng 65:1912–1923

    Article  Google Scholar 

  26. Ma W, Yu S, Ma K, Wang J, Ding X, Zheng Y (2019) Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification. Med Image Comput Comput Assist Interv, pp 769–778

  27. Cherukuri V, Bg VK, Bala R, Monga V (2020) Deep retinal image segmentation with regularization under geometric priors. IEEE Trans Image Process 29:2552–2567

  28. Park KB, Choi SH, Lee JY (2020) M-GAN: Retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks. IEEE Access 8:146308–146322

    Article  Google Scholar 

  29. Reza AM (2004) Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Sig Proc Syst 38:35–44

    Article  Google Scholar 

  30. Long J, Shelhamer E, Darrell T (2014) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39:640–651

    Google Scholar 

  31. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Med Image Comput Comput Assist Interv, pp 234–241

  32. Lin T, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection. In: IEEE Int. Conf. Comput. Vision, pp 2980–2988

  33. Ostu N (1979) A threshold selection method from gray-histogram. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  34. Li X, Jiang Y, Li M, Yin S (2020) Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans Ind Inf 17:1958–1967

    Article  Google Scholar 

  35. Cheng E, Du L, Wu Y, Zhu YJ, Megalooikonomou V (2014) Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features. Mach Vis Appl 25:1779–1792

    Article  Google Scholar 

  36. Li L, Verma M, Nakashima Y, Nagahara H, Kawasaki P (2020) Iternet: retinal image segmentation utilizing structural redundancy in vessel networks. In: IEEE/CVF Winter. Conf. Appl. Comput. Vision, pp 3656–3665

  37. Orlando JI, Prokofyeva E, Blaschko MB (2017) A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans Biomed Eng 64:16–27

    Article  Google Scholar 

  38. You X, Peng Q, Yuan Y, Cheng Y, Lei J (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit 44:2314–2334

    Article  Google Scholar 

  39. Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative vessel segmentation of fundus images. IEEE Trans Biomed Eng 62:1738–1749

    Article  Google Scholar 

  40. George A, Nicola S, Mario V, Nicolai P (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19:46–57

    Article  Google Scholar 

  41. Yin B, Li H, Sheng B, Hou X, Chen Y, Wu W, Li P, Shen R, Bao Y, Jia W (2015) Vessel extraction from non-fluorescein fundus images using orientation-aware detector. Med Image Anal 26:232–242

    Article  Google Scholar 

  42. Zhang J, Dashtbozorg B, Bekkers E, Pluim JPW, Duits R, Romeny BMTH (2016) “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans Med Imaging 35:2631–2644

    Article  Google Scholar 

  43. Xia H, Jiang F (2018) Mapping functions driven robust retinal vessel segmentation via training patches. IEEE Access. 6:61973–61982

    Article  Google Scholar 

  44. Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv e-prints, arXiv: 1802.06955

  45. Marin D, Aquino A, Gegundez-Arias ME, Bravo JM (2011) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 30:146–158

    Article  Google Scholar 

  46. Abbas W, Shakeel MH, Khurshid N, Taj M (2019) Patch-based generative adversarial network towards retinal vessel segmentation. In: Int. Conf. Neural. Inform. Process., pp 49–56

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61862030, No. 62072218, and 62261025), by the Natural Science Foundation of Jiangxi Province (No. 20192ACB20002 and No. 20192ACBL21008), and by the Talent project of Jiangxi Thousand Talents Program (No. jxsq2019201056), and by the Project of the Education Department of Jiangxi Province (No. GJJ200541), and by the Postdoctoral Research Projects of Jiangxi Province (No. 2020KY44).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

    Yong Yang

  2. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Weiguo Wan

  3. School of Software, Tiangong University, Tianjin, 300387, China

    Shuying Huang

  4. School of Information Technology, Jiangxi University of Finance and Economics, Nancahng, 330013, China

    Xin Zhong & Xiangkai Kong

Authors
  1. Yong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiguo Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangkai Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Weiguo Wan or Shuying Huang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Wan, W., Huang, S. et al. RADCU-Net: residual attention and dual-supervision cascaded U-Net for retinal blood vessel segmentation. Int. J. Mach. Learn. & Cyber. 14, 1605–1620 (2023). https://doi.org/10.1007/s13042-022-01715-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-022-01715-3

Keywords

Search

Navigation