[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Efficient Segmentation of Vessels and Disc Simultaneously Using Multi-channel Generative Adversarial Network

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Two-dimensional pictorial representation of the rear part of human eye furnish diagnostic information about blood vessels, optic disc, and macula. Among these features vessel analysis at disc provides quantitative diagnostic information about different stages of ophthalmic diseases. The alteration in morphology of retinal blood vessels in the neighbourhood of disc region convey important diagnostic measures related to cure, and assessment of cardiovascular and ophthalmologic diseases (glaucoma, diabetic retinopathy, and arteriosclerosis). Hence, vessel analysis near by disc becomes a prime factor for analysis of ophthalmic diseases. Here, a multi-channel generative adversarial network is used for simultaneous segmentation of retinal vessels and disc. The model simultaneously segments retinal landmarks through a single generative adversarial network (GAN) using adversarial learning process. Multi-scale residual convolutional neural network (MSR-Net) is utilized as generator which is capable of generating two channel segmentation maps (vessels and disc region) separately. In the discriminator section, two branches of convolutional neural network (CNN)-based binary classifiers are used. The segmentation performance is evaluated on two publicly available databases namely CHASE_DB1, HRF databases and DRIVE database. Different quantitative performance measures are conducted to compare the performance of the proposed method with state-of-the-art methods. The projected work achieved an accuracy of 0.9730 for HRF data set, 0.9861 for CHASE_DB1 data set and 0.9816 for DRIVE data set for segmenting blood vessels. Simultaneously, this method achieved an accuracy of 0.9982 for HRF data set, 0.9965 for CHASE_DB1 data set and 0.9968 for DRIVE data set for segmenting disc region. The proposed method can be used for analyzing diagnostic information about ophthalmic diseases like glaucoma and visual field defects cause due to presence of abnormalities in the optic disc region.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

References

  1. Akil H, Huang AS, Francis BA, Sadda SR, Chopra V. Retinal vessel density from optical coherence tomography angiography to differentiate early glaucoma, pre-perimetric glaucoma and normal eyes. PLoS One. 2017;12(2):1–12. https://doi.org/10.1371/journal.pone.0170476

    Article  CAS  Google Scholar 

  2. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention. Cham: Springer; 2015. https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  3. Yan Z, Yang X, Cheng, KT. Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng. 2018;65(9):1912–1923. https://doi.org/10.1109/TBME.2018.2828137

    Article  PubMed  Google Scholar 

  4. Uysal E, Guraksin GE. Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks. Multimed Tools Appl. 2020;80:1929–58. https://doi.org/10.1007/s11042-020-09372-w

    Article  Google Scholar 

  5. Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J. CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging. 2019;38(10):2281–92. https://doi.org/10.1109/TMI.2019.2903562

    Article  PubMed  Google Scholar 

  6. Fu H, Xu Y, Lin S, Wong DWK, Liu J. DeepVessel: Retinal vessel segmentation via deep learning and conditional random field. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17–21, 2016, Proceedings, Part II 19. Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-46723-8_16

  7. Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X. Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. J Neurocomput. 2018;309:179–91. https://doi.org/10.1016/j.neucom.2018.05.011

    Article  Google Scholar 

  8. Shin SY, Lee S, Yun ID, Lee KM. Deep vessel segmentation by learning graphical connectivity. Med Image Anal. 2019;58:1–14.

    Article  Google Scholar 

  9. Zhang H, Goodfellow I, Metaxas D, Odena A. Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp 7354-7363, PMLR; 2019. https://doi.org/10.48550/arXiv.1805.0831

  10. Sreng S, Maneerat N, Hamamoto K, YadanarWin K. Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. In: MDIP; 2020. p. 1–19. https://doi.org/10.1155/2019/4061313

  11. Latif J, Tu S, Ur Rehman S, Imran A, Latif Y. ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN Appl Sci. 2022;4(98):1–11. https://doi.org/10.1007/s42452-022-04984-3

    Article  ADS  Google Scholar 

  12. Owen CG, Rudnicka AR, Mullen R, Barman SA, Monekosso DN, Whincup PH, 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. Investig Ophthalmol Vis Sci. 2009;50(5):2004–10.

    Article  Google Scholar 

  13. Budai A, Bock R, Maier A, Hornegger J, Michelson G. Robust vessel segmentation in fundus images. Int J Biomed Imaging. 2013;2013:1–12. https://doi.org/10.1155/2013/154860

    Article  Google Scholar 

  14. Emami H, Dong M, Nejad-Davarani S, Glide-Hurst C. Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med Phys. 2018;6:1–21. https://doi.org/10.1002/mp.13047

    Article  Google Scholar 

  15. Wan C, Zhou X, You Q, Sun J, Shen J, Zhu S, Jiang Q, Yang W. Retinal image enhancement using cycle-constraint adversarial network. Front Med. 2022;8:1–16. https://doi.org/10.3389/fmed.2021.793726

    Article  CAS  Google Scholar 

  16. Li J, Liang X, Wei Y, Xu T, Feng J, Yan S. Perceptual generative adversarial networks for small object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1222–1230; 2017. https://doi.org/10.1109/CVPR.2017.211

  17. Son J, Park SJ, Jung K-H. Retinal vessel segmentation in fundoscopic images with generative adversarial networks. ArXiv. 2017;abs/1706.09318:1–9. https://api.semanticscholar.org/CorpusID:31464468

    ADS  Google Scholar 

  18. Xue Y, Xu T, Zhang H, Long LR, Huang X. Segan: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics. 2018;16:383–92. https://doi.org/10.48550/arXiv.1706.01805

    Article  PubMed  Google Scholar 

  19. Guo X, Chen C, Lu Y, Meng K, Chen H, Zhou K, Wang Z, Xiao R. Retinal vessel segmentation combined with generative adversarial networks and dense u-net. IEEE Access. 2020;8:194551–60. https://doi.org/10.1109/ACCESS.2020.3033273

    Article  Google Scholar 

  20. Park K-B, Choi SH, Lee JY. M-GAN: retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks. IEEE Access. 2020;8:146308–22. https://doi.org/10.1109/ACCESS.2020.3015108

    Article  Google Scholar 

  21. Deng X, Ye J. A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network. Biomed Signal Process Control. 2022;73:103467.

    Article  Google Scholar 

  22. Chen D, Yang W, Wang L, Tan S, Lin J, Bu W. PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation. PLoS One. 2022;17(1):1–22. https://doi.org/10.1371/journal.pone.0262689

    Article  CAS  Google Scholar 

  23. Kar M, Neog DR, Nath M. Retinal vessel segmentation using multi-scale residual convolutional neural network (MSR-Net) combined with generative adversarial networks. Circuits Syst Signal Process. 2022;42(2):1206–35. https://doi.org/10.1007/s00034-022-02190-5

    Article  Google Scholar 

  24. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol 2, pp 2672–2680; 2014. https://doi.org/10.48550/arXiv.1406.2661

    Google Scholar 

  25. Niemeijer M, Ginneken B, Loog M. Comparative study of retinal vessel segmentation methods on a new publicly available database. Proc SPIE Int Soc Opt Eng. 2004;5370:648–656. https://doi.org/10.1117/12.535349

    Article  ADS  Google Scholar 

  26. Wang Z, Bovik A, Sheikh H, Simoncelli E. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600–12.

    Article  ADS  PubMed  Google Scholar 

  27. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Adv Neural Inf Process Syst. 2017;30:1–38.

    Google Scholar 

  28. Dowson DC, Landau BV. The Fréchet distance between multivariate normal distributions. J Multivar Anal. 1982;12(3):450–55.

    Article  Google Scholar 

  29. Roychowdhury S, Koozekanani DD, Kuchinka SN, Parhi KK. Optic disc boundary and vessel origin segmentation of fundus images. IEEE J Biomed Health Inform. 2016;20(6):1562–74.

    Article  PubMed  Google Scholar 

  30. Abdullah M, Fraz MM, Barman SA. Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm. PeerJ. 2016;4:1–22. https://doi.org/10.7717/peerj.2003

    Article  Google Scholar 

  31. Basit A, Fraz MM. Optic disc detection and boundary extraction in retinal images. Appl Opt. 2015;54:3440–7.

    Article  ADS  CAS  PubMed  Google Scholar 

  32. Jin Q, Meng Z, Pham TD, Chen Q, Wei L, Su R. DUNet: a deformable network for retinal vessel segmentation. Knowl Based Syst. 2019;14:1–12. https://doi.org/10.1016/j.knosys.2019.04.025

    Article  CAS  Google Scholar 

  33. Mathieu M, Couprie C, LeCun Y (2016) Deep multi-scale video prediction beyond mean square error. In: 4th International conference on learning representations, ICLR 2016 - San Juan, Puerto Rico, pp 1–25. https://doi.org/10.48550/arXiv.1511.05440

  34. Cheng B, Girshick RB, Dollár P, Berg AC, Kirillov A. Boundary IOU: improving object-centric image segmentation evaluation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp 15329–15337; 2021. https://doi.org/10.1109/CVPR46437.2021.01508

  35. Girshick R. Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp 1440–1448; 2015. https://doi.org/10.1109/ICCV.2015.169

  36. Lin T, Goyal P, Girshick RB, He K, Dollár P. Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, pp 2999–3007; 2017. https://doi.org/10.1109/ICCV.2017.324

Download references

Acknowledgements

The work was carried out in the research lab of ECE department, National Institute of Technology Puducherry, India.

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India

    Mithun Kumar Kar

  2. Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal, 609609, India

    Malaya Kumar Nath

Authors
  1. Mithun Kumar Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malaya Kumar Nath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mithun Kumar Kar.

Ethics declarations

Conflict of Interest

The proposed work fulfill the conformity with ethical standards. There is no conflicts of interest.

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

Kar, M.K., Nath, M.K. Efficient Segmentation of Vessels and Disc Simultaneously Using Multi-channel Generative Adversarial Network. SN COMPUT. SCI. 5, 288 (2024). https://doi.org/10.1007/s42979-024-02610-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-02610-0

Keywords

Search

Navigation