Skip to main content
SpringerLink
Log in

Unpaired and Self-supervised Optical Coherence Tomography Angiography Super-Resolution

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

Included in the following conference series:

  • 1489 Accesses

Abstract

Optical coherence tomography angiography (OCTA) is usually used to observe the blood flow information of retina and choroid. It is meaningful for clinicians to observe more microvascular details by enhancing the resolution of OCTA images, which is conducive to the diagnosis of diseases. However, due to the limitation of imaging equipment, when the resolution of OCTA is improved, the field of view (FOV) will be reduced. In the existing methods to enhance the resolution of OCTA, paired training data from the same eye are generally required, but paired data are usually difficult to be obtained, and the resolution of enhanced images is difficult to exceed that of original high resolution (3 × 3 mm2) OCTA images. Therefore, to improve the resolution of low resolution (6 × 6 mm2) OCTA images, this paper proposes an unpaired and self-supervised OCTA super-resolution (USOSR) method by down sampling and enhancing the original 3 × 3 mm2 OCTA images. Experimental results demonstrate that the enhanced 6 × 6 mm2 OCTA images have significantly stronger contrast, sharper edges and higher information entropy than the original images.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Or, C., Sabrosa, A.S., Sorour, O., Arya, M., Waheed, N.: Use of OCTA, FA, and ultra-widefield imaging in quantifying retinal ischemia: a review. Asia Pac. J. Ophthalmol. 7(1), 46–51 (2018)

    Google Scholar 

  2. Waheed, N.K., Moult, E.M., Fujimoto, J.G., Rosenfeld, P.J.: Optical coherence tomography angiography of dry age-related macular degeneration. OCT Angiogr. Retin. Macular Dis. 56, 91–100 (2016)

    Article  Google Scholar 

  3. Huang, D., Jia, Y., Rispoli, M., Tan, O., Lumbroso, B.: OCT angiography of time course of choroidal neovascularization in response to anti-angiogenic treatment. Retina 35(11), 2260 (2015)

    Google Scholar 

  4. O’Bryhim, B.E., Apte, R.S., Kung, N., Coble, D., Van Stavern, G.P.: Association of preclinical Alzheimer disease with optical coherence tomographic angiography findings. JAMA Ophthalmol. 136(11), 1242–1248 (2018)

    Article  Google Scholar 

  5. Liu, Y., Qiao, Y., Hao, Y., Wang, F., Rashid, S.F.: Single image super resolution techniques based on deep learning: status, applications and future directions. J. Image Graph. 9(3) (2021)

    Google Scholar 

  6. Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114–125 (2017)

    Google Scholar 

  7. Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., Zelnik-Manor, L.: The 2018 PIRM challenge on perceptual image super-resolution. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 334–355. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_21

    Chapter  Google Scholar 

  8. Mahapatra, D., Bozorgtabar, B., Hewavitharanage, S., Garnavi, R.: Image super resolution using generative adversarial networks and local saliency maps for retinal image analysis. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 382–390. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_44

  9. Du, J., et al.: Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network. Neurocomputing 392, 209–220 (2020)

    Article  Google Scholar 

  10. Gao, M., Guo, Y., Hormel, T.T., Sun, J., Hwang, T.S., Jia, Y.: Reconstruction of high-resolution 6×6-mm OCT angiograms using deep learning. Biomed. Opt. Express 11(7), 3585–3600 (2020)

    Article  Google Scholar 

  11. Zhou, T., et al.: Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning. OSA Contin. 3(6), 1664–1678 (2020)

    Google Scholar 

  12. Zhao, C., Dewey, B.E., Pham, D.L., Calabresi, P.A., Reich, D.S., Prince, J.L.: SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning. IEEE Trans. Med. Imaging 40(3), 805–817 (2020)

    Article  Google Scholar 

  13. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  14. Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3147–3155 (2017)

    Google Scholar 

  15. Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4539–4547 (2017)

    Google Scholar 

  16. Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., Timofte, R.: Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell. (2021)

    Google Scholar 

  17. Li, M., et al.: IPN-V2 and octa-500: methodology and dataset for retinal image segmentation. arXiv preprint arXiv:2012.07261 (2020)

  18. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  19. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  20. Brenner, J.F., Dew, B.S., Horton, J.B., King, T., Neurath, P.W., Selles, W.D.: An automated microscope for cytologic research a preliminary evaluation. J. Histochem. Cytochem. 24(1), 100–111 (1976)

    Article  Google Scholar 

  21. Peli, E.: Contrast in complex images. JOSA A 7(10), 2032–2040 (1990)

    Google Scholar 

  22. Vollath, D.: The influence of the scene parameters and of noise on the behaviour of automatic focusing algorithms. J. Microsc. 151(2), 133–146 (1988)

    Article  Google Scholar 

  23. Rényi, A.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, vol. 4, pp. 547–562. University of California Press (1961)

    Google Scholar 

  24. Kim, J., Lee, J. K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

    Google Scholar 

Download references

Acknowledgment

This study was supported by National Natural Science Foundation of China (62172223, 61671242), and the Fundamental Research Funds for the Central Universities (30921013105).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Chaofan Zeng & Qiang Chen

  2. Department of Ophthalmology, The First Affiliated Hospital With Nanjing Medical University, Nanjing, 210094, China

    Songtao Yuan

Authors
  1. Chaofan Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Songtao Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Chen .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 747 kb)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Zeng, C., Yuan, S., Chen, Q. (2022). Unpaired and Self-supervised Optical Coherence Tomography Angiography Super-Resolution. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18916-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18915-9

  • Online ISBN: 978-3-031-18916-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation