A deep learning based pipeline for optical coherence tomography angiography

Journal article

Publication Details

Author(s): Liu X, Huang Z, Wang Z, Wen C, Jiang Z, Yu Z, Liu J, Liu G, Huang X, Maier A, Ren Q, Lu Y
Journal: Journal of Biophotonics
Publication year: 2019
ISSN: 1864-063X
eISSN: 1864-0648


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.

FAU Authors / FAU Editors

Lu, Yanye
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)

External institutions with authors

Beijing Friendship Hospital
Peking University (PKU) / 北京大学
Shanghai Jiao Tong University / 上海交通大学

How to cite

Liu, X., Huang, Z., Wang, Z., Wen, C., Jiang, Z., Yu, Z.,... Lu, Y. (2019). A deep learning based pipeline for optical coherence tomography angiography. Journal of Biophotonics. https://dx.doi.org/10.1002/jbio.201900008

Liu, Xi, et al. "A deep learning based pipeline for optical coherence tomography angiography." Journal of Biophotonics (2019).


Last updated on 2019-16-07 at 08:38