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Optical coherence tomography image de-noising using a generative adversarial network with speckle modulation.

Abstract

Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to de-noise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. 210 000 SM-OCT images were used for training and validating the neural network model, which we call SM-GAN. The performance of the SM-GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM-GAN model was compared to traditional OCT de-noising methods and other state-of-the-art deep learning based denoise networks. We conclude that the SM-GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions. This article is protected by copyright. All rights reserved.

Authors+Show Affiliations

Department of Electrical and Computer Engineering, Lehigh University, 27 Memorial Drive W, Bethlehem, PA, USA. Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr., St. Louis, MO, USA.Department of Dermatology, Affiliated Hospital of Weifang Medical University, Weifang, China. Department of Bioengineering, Lehigh University, 111 Research Drive, Bethlehem, PA, USA.Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr., St. Louis, MO, USA.Department of Electrical and Computer Engineering, Lehigh University, 27 Memorial Drive W, Bethlehem, PA, USA. Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr., St. Louis, MO, USA.Department of Electrical and Computer Engineering, Lehigh University, 27 Memorial Drive W, Bethlehem, PA, USA.Department of Electrical and Computer Engineering, Lehigh University, 27 Memorial Drive W, Bethlehem, PA, USA. Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr., St. Louis, MO, USA. Department of Bioengineering, Lehigh University, 111 Research Drive, Bethlehem, PA, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31970879

Citation

Dong, Zhao, et al. "Optical Coherence Tomography Image De-noising Using a Generative Adversarial Network With Speckle Modulation." Journal of Biophotonics, 2020.
Dong Z, Liu G, Ni G, et al. Optical coherence tomography image de-noising using a generative adversarial network with speckle modulation. J Biophotonics. 2020.
Dong, Z., Liu, G., Ni, G., Jerwick, J., Duan, L., & Zhou, C. (2020). Optical coherence tomography image de-noising using a generative adversarial network with speckle modulation. Journal of Biophotonics, doi:10.1002/jbio.201960135.
Dong Z, et al. Optical Coherence Tomography Image De-noising Using a Generative Adversarial Network With Speckle Modulation. J Biophotonics. 2020 Jan 22; PubMed PMID: 31970879.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Optical coherence tomography image de-noising using a generative adversarial network with speckle modulation. AU - Dong,Zhao, AU - Liu,Guoyan, AU - Ni,Guangming, AU - Jerwick,Jason, AU - Duan,Lian, AU - Zhou,Chao, Y1 - 2020/01/22/ PY - 2019/10/02/received PY - 2019/12/23/revised PY - 2020/01/15/accepted PY - 2020/1/24/entrez KW - De-noise KW - Deep learning KW - Generative Adversarial Network KW - Optical Coherence Tomography JF - Journal of biophotonics JO - J Biophotonics N2 - Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to de-noise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. 210 000 SM-OCT images were used for training and validating the neural network model, which we call SM-GAN. The performance of the SM-GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM-GAN model was compared to traditional OCT de-noising methods and other state-of-the-art deep learning based denoise networks. We conclude that the SM-GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions. This article is protected by copyright. All rights reserved. SN - 1864-0648 UR - https://www.unboundmedicine.com/medline/citation/31970879/Optical_coherence_tomography_image_de-noising_using_a_generative_adversarial_network_with_speckle_modulation L2 - https://doi.org/10.1002/jbio.201960135 DB - PRIME DP - Unbound Medicine ER -