A Deep Learning Based Pipeline for Optical Coherence Tomography Angiography.

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Abstract

Optical Coherence Tomography Angiography (OCTA) is a relatively new imaging modality that generates microvasculature map. Meanwhile, deep learning has been recently attracting considerable attention in image-to-image translation, such as image denoising, super-resolution, and prediction. In this paper, we propose a deep learning based pipeline for OCTA. This pipeline consists of three parts: training data preparation, model learning, and OCTA predicting using the trained model. To be mentioned, the datasets used in this work were automatically generated by a conventional system setup without any expert labelling. Promising results have been validated by in-vivo animal experiments, which demonstrate that deep learning is able to outperform traditional OCTA methods. The image quality is improved in not only higher signal-to-noise ratio (SNR) but also better vasculature connectivity by laser speckle eliminating, showing potential in clinical use. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.