Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Zhao Dong, Guoyan Liu, Guangming Ni, Jason Jerwick, Lian Duan, Chao Zhou

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

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 denoise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. In total, 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 denoising 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.

Original languageEnglish
Article numbere201960135
JournalJournal of Biophotonics
Volume13
Issue number4
DOIs
StatePublished - Apr 1 2020

Keywords

  • de-noise
  • deep learning
  • generative adversarial network
  • optical coherence tomography

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