Paper
11 August 2023 High quality optical coherence tomography imaging of mock cataract surgery with deep-learning-based denoising
Author Affiliations +
Abstract
Optical coherence tomography can provide visualizations of the eye both in diagnostic and surgical settings. However, noise limits the achievable image quality, especially in scenarios in which multi-frame averaging is not available. In this work, we present high-quality OCT image denoising using deep learning, only requiring unpaired volumetric capture scans for training. It is shown that, by exploiting neighboring B-scans, an artificial neural network for denoising OCT images can be trained based on a state-of-the-art approach which usually requires repeated scans from the exact same location. The effect of denoising is demonstrated for B-scans and volumetric renderings during and after mock cataract surgery on ex-vivo porcine eyes.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jonas Nienhaus, Anja Britten, Philipp Matten, Thomas Schlegl, Katharina Dettelbacher, Andreas Pollreisz, Wolfgang Drexler, Rainer A. Leitgeb, and Tilman Schmoll "High quality optical coherence tomography imaging of mock cataract surgery with deep-learning-based denoising", Proc. SPIE 12632, Optical Coherence Imaging Techniques and Imaging in Scattering Media V, 126321Q (11 August 2023); https://doi.org/10.1117/12.2670877
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KEYWORDS
Coherence imaging

Denoising

Optical coherence tomography

Cataracts

Surgery

Education and training

Optical imaging

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