Optical Coherence Tomography (OCT) is one of the most important retinal imaging technique. However, involuntary motion artifacts still pose a major challenge in OCT imaging that compromises the quality of downstream analysis, such as retinal layer segmentation and OCT Angiography. We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single volumetric scan. The proposed method consists of two fully-convolutional neural networks that predict Z and X dimensional displacement maps sequentially in two stages. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods. Specifically, the method can recover the overall curvature of the retina, and can be generalized well to various diseases and resolutions.
@article{arxiv.2305.18361,
title = {Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging},
author = {Yiqian Wang and Alexandra Warter and Melina Cavichini and Varsha Alex and Dirk-Uwe G. Bartsch and William R. Freeman and Truong Q. Nguyen and Cheolhong An},
journal= {arXiv preprint arXiv:2305.18361},
year = {2023}
}