A deep learning based pipeline for optical coherence tomography angiography. Issue 10 (1st July 2019) - Liu, Xi; Huang, Zhiyu; Wang, Zhenzhou; Wen, Chenyao; Jiang, Zhe; Yu, Zekuan; Liu, Jingfeng; Liu, Gangjun; Huang, Xiaolin; Maier, Andreas; Ren, Qiushi; Lu, Yanye
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 labeling. 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 but also better vasculature connectivity by laser speckle eliminating, showing potential in clinical use. Schematic description of the deep learning based optical coherent tomography angiography pipeline. Abstract : To improve the image quality of optical coherence tomography angiography, we propose a deep learning based pipeline in the reconstruction stage. Such pipeline is able to mine more intrinsic information from optical coherent tomography signals, outperforming conventional analytic algorithms. Results show that image quality is improved in not only higher signal‐to‐noise ratio but also better vasculature connectivity, indicating promising potential towards clinical applications.