Fast optical coherence tomography image enhancement using deep learning for smart laser surgery: Preliminary study in bone tissue

Yakub A. Bayhaqi, Georg Router, Alexander Navarini, Philippe C. Cattin, Azhar Zam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the most common image denoising technique used in Optical Coherence Tomography (OCT) is the frame averaging method. Inherent to this method is that the more images are used, the better the resulting image. This approach comes, however, at the price of increased acquisition time and introduced sensitivity to motion artifacts. To overcome these limitations, we proposed an artificial neural network architecture able to imitate an averaging method using only a single image frame. The reconstructed image has an improvement in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) parameters compared to the original image. Additionally, we also observed an improvement in the sharpness of the denoised images. This result shows the possibility to use this method as a pre-processing step for real-time tissue classification in smart laser surgery especially in bone surgery.

Original languageEnglish (US)
Title of host publicationFourth International Conference on Applications of Optics and Photonics
EditorsManuel F. M. Costa
PublisherSPIE
ISBN (Electronic)9781510631632
DOIs
StatePublished - 2019
Event4th International Conference on Applications of Optics and Photonics, AOP 2019 - Lisbon, Portugal
Duration: May 31 2019Jun 4 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11207
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference4th International Conference on Applications of Optics and Photonics, AOP 2019
Country/TerritoryPortugal
CityLisbon
Period5/31/196/4/19

Keywords

  • Denoising
  • Frame Averaging
  • Neural Network
  • Optical Coherence Tomography

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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