3D-Unet deep learning algorithm for denoising OCTA volumes acquired at MHz A-scan rates

Michael Niederleithner; Anja Britten; Philipp Matten; Niranchana Manivannan; Aditya Nair; Lars Omlor; Wolfgang Drexler; Rainer Leitgeb; Tilman Schmoll

doi:10.1117/12.2583189

5 March 2021 3D-Unet deep learning algorithm for denoising OCTA volumes acquired at MHz A-scan rates

Michael Niederleithner, Anja Britten, Philipp Matten, Niranchana Manivannan, Aditya Nair, Lars Omlor, Wolfgang Drexler, Rainer Leitgeb, Tilman Schmoll

Author Affiliations +

Proceedings Volume 11623, Ophthalmic Technologies XXXI; 1162319 (2021) https://doi.org/10.1117/12.2583189
Event: SPIE BiOS, 2021, Online Only

Abstract

Increasing the FOV of OCTA images while keeping the acquisition time moderate requires high A-scan rates. Therefore, OCTA images appear to be noisier. Deep learning methods can be used for noise reduction. In OCTA volumes small vessels with an orientation perpendicular to the image plane are often removed by deep learning denoising algorithms, due to their small appearance. To overcome this a 3-dimensional Unet was developed to utilize volumetric information. With the knowledge of also the third dimension, the algorithm is able to distinguish between noise and vessel contrast and is therefore less likely to remove vessels.

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Michael Niederleithner, Anja Britten, Philipp Matten, Niranchana Manivannan, Aditya Nair, Lars Omlor, Wolfgang Drexler, Rainer Leitgeb, and Tilman Schmoll "3D-Unet deep learning algorithm for denoising OCTA volumes acquired at MHz A-scan rates", Proc. SPIE 11623, Ophthalmic Technologies XXXI, 1162319 (5 March 2021); https://doi.org/10.1117/12.2583189

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