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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