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Speckle denoising of optical coherence tomography image using residual encoder–decoder CycleGAN

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Abstract

Optical coherence tomography (OCT) is a powerful technology for monitoring and diagnosing eye diseases. However, speckle noise is not beneficial for improving OCT image quality and further image analysis, such as segmentation of the retinal layer. Inspired by the rapid development of deep learning, several methods have been proposed for OCT denoising, and promising results have been obtained. Nevertheless, most methods are supervised and require paired noisy and clean images. In clinical practice, this requirement is too difficult to meet since clean images are usually obtained by averaging consecutive frames in the same location, which may lead to motion or detail blurring. To address this problem, we propose an unsupervised learning technique based on a cycle-consistent adversarial network that removes speckle noise from OCT images by learning a map from the noisy phase to the clean phase. In addition, we interrogate our approach with extensive quantitative and qualitative metrics and compare it with several state-of-the-art methods. The results of the experiments indicate that the proposed method shows better speckle reduction performance than traditional methods and most deep learning methods.

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Availability of data and materials

All optical coherence tomography images used for model training and internal validation are publicly available online through https://people.duke.edu/sf59/Fang_BOE_2012.htm

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Acknowledgements

This research was partly funded by National Natural Science Foundation of China (NSFC) Grant Number 61871277, partly funded by Sichuan Science and Technology Program Grant Number 2019YFH0193 and partly funded by Chengdu Science and Technology Program Grant Number 2018YF0500069SN.

Funding

National Natural Science Foundation of China (NSFC) (61871277); Sichuan Science and Technology Program (2019YFH0193); Chengdu Science and Technology Program (2018YF0500069SN).

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Authors and Affiliations

  1. College of Computer Science and College of Software Engineering, Sichuan University, South 1st Ring Road, Chengdu, 610041, Sichuan, China

    Kang Xie, Hu Chen, Mingming Yang & Yuhua He

  2. Department of National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, South 1st Ring Road, Chengdu, 610041, Sichuan, China

    Mengting Luo & Hu Chen

  3. Department of Scientific Research and Education, The Sixth People’s Hospital of Chengdu, Jianshe South Street, Chengdu, 610041, Sichuan, China

    Peixi Liao

  4. Department of Cyber Science and Engineering, Sichuan University, South 1st Ring Road, Chengdu, 641421, Sichuan, China

    Yi Zhang

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  1. Kang Xie
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Contributions

Conceptualization was carried out by MY and YH; methodology was performed by KX; software was carried out by KX; validation was took part by KX, ML and MY; formal analysis was performed by HC; investigation was carried out by PL; resources were carried out by HC; data curation was took part by PL; writing—original draft preparation—was performed by KX; writing—-review and editing—was performed by HC; visualization was took part by MY; supervision was carried out by YZ; project administration was carried out by HC; funding acquisition was carried out by HC. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Hu Chen.

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Informed Consent Statement: The public OCT dataset used in this study was originally collected 326 from the four A2A SDOCT clinics (the Devers Eye Institute, Duke Eye Center, Emory Eye Center, and 327 National Eye Institute) under their IRB approval.

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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.

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Xie, K., Luo, M., Chen, H. et al. Speckle denoising of optical coherence tomography image using residual encoder–decoder CycleGAN. SIViP 17, 1521–1533 (2023). https://doi.org/10.1007/s11760-022-02361-6

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