A deep learning based pipeline for optical coherence tomography angiography

Liu X, Huang Z, Wang Z, Wen C, Jiang Z, Yu Z, Liu J, Liu G, Huang X, Maier A, Ren Q, Lu Y (2019)

Publication Type: Journal article

Publication year: 2019


Article Number: e201900008

DOI: 10.1002/jbio.201900008


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.

Authors with CRIS profile

Involved external institutions

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://doi.org/10.1002/jbio.201900008


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

BibTeX: Download