Self-supervised denoising using optimized blind-spot networks for real-time application in 4D-OCT

Jonas Nienhaus; Philipp Matten; Anja Britten; Thomas Schlegl; Eva Höck; Alexander Freytag; Matt Everett; Nancy Hecker-Denschlag; Wolfgang Drexler; Rainer A. Leitgeb; Tilman Schmoll

doi:10.1117/12.2653479

3 April 2023 Self-supervised denoising using optimized blind-spot networks for real-time application in 4D-OCT

Jonas Nienhaus, Philipp Matten, Anja Britten, Thomas Schlegl, Eva Höck, Alexander Freytag, Matt Everett, Nancy Hecker-Denschlag, Wolfgang Drexler, Rainer A. Leitgeb, Tilman Schmoll

Author Affiliations +

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641V (2023) https://doi.org/10.1117/12.2653479
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

As in other imaging modalities, noise decreases image quality in optical coherence tomography (OCT), which is especially problematic in real-time intra-surgical application, where multi-frame averaging is not available. In this work, we present an adapted self-supervised training approach to train a blind-spot denoising network for OCT data. With the proposed method, the stability of the method is improved, avoiding the occurrence of artifacts by increasing realism of training data. We show that using this approach, the quality of two-dimensional B-scans can be improved qualitatively and quantitatively even without paired training data. This improvement is also translated into live volumetric renderings composed of denoised two-dimensional scans, even when using only very small network complexities due to harsh time constraints.

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Jonas Nienhaus, Philipp Matten, Anja Britten, Thomas Schlegl, Eva Höck, Alexander Freytag, Matt Everett, Nancy Hecker-Denschlag, Wolfgang Drexler, Rainer A. Leitgeb, and Tilman Schmoll "Self-supervised denoising using optimized blind-spot networks for real-time application in 4D-OCT", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641V (3 April 2023); https://doi.org/10.1117/12.2653479

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