We present a novel approach of leveraging deep learning to reconstruct high-resolution OCT B-scans from reduced axial resolution data. In this work, the original OCT signal is used as the ground truth, and lower resolution was simulated by windowing the interference fringes. A super-resolution pixel-to-pixel generative adversarial network (GAN) was investigated for reconstructing high-resolution OCT data in the spatial domain and is compared against reconstructing in the spectral domain.
We present novel approaches of implementing state-of-the-art deep learning techniques for the processing of optical coherence tomography angiography (OCT-A) images for the classification of diabetic retinopathy (DR) severity. The effects of feature-engineering on a deep neural network’s classification performance is compared against unprocessed OCT-A images. We investigate the effects of lower axial resolution (simulated by using a narrower spectral bandwidth) on the classification of DR severity, and the recovery of lost features using a generative adversarial network. We also explore the relationship between DR severity classification and lateral resolution.
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