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9 March 2023 Learning to reconstruct structural OCT images from raw data using deep learning
Thomas Schlegl, Ali Salehi, Matt Everett, Niranchana Manivannan, Homayoun Bagherinia, Michael Niederleithner, Andreas Pollreisz M.D., Wolfgang Drexler, Rainer Leitgeb, Tilman Schmoll
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Proceedings Volume PC12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII; PC123671B (2023) https://doi.org/10.1117/12.2652557
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
In this work, we propose to utilize an end-to-end deep learning approach for the reconstruction of structural OCT images based on the rich information contained in raw OCT data alone instead of performing signal processing with manual tuning of the associated system parameters. The proposed deep learning approach already yields promising results on a small training data set of widefield OCT images. Qualitative results suggest that the neural network is able to implicitly learn the full signal processing pipeline and its inherent system parameters but is strongly impacted by the data variability seen during training.
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Thomas Schlegl, Ali Salehi, Matt Everett, Niranchana Manivannan, Homayoun Bagherinia, Michael Niederleithner, Andreas Pollreisz M.D., Wolfgang Drexler, Rainer Leitgeb, and Tilman Schmoll "Learning to reconstruct structural OCT images from raw data using deep learning", Proc. SPIE PC12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII, PC123671B (9 March 2023); https://doi.org/10.1117/12.2652557
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KEYWORDS
Optical coherence tomography
Neural networks
Signal processing
Associative arrays
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