@inproceedings{2b8fbe9576c94b1a9e6a63036d150934,
title = "Fast optical coherence tomography image enhancement using deep learning for smart laser surgery: Preliminary study in bone tissue",
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.",
keywords = "Denoising, Frame Averaging, Neural Network, Optical Coherence Tomography",
author = "Bayhaqi, {Yakub A.} and Georg Router and Alexander Navarini and Cattin, {Philippe C.} and Azhar Zam",
note = "Publisher Copyright: {\textcopyright} 2019 SPIE.; 4th International Conference on Applications of Optics and Photonics, AOP 2019 ; Conference date: 31-05-2019 Through 04-06-2019",
year = "2019",
doi = "10.1117/12.2527293",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Costa, {Manuel F. M.}",
booktitle = "Fourth International Conference on Applications of Optics and Photonics",
}