Deep learning network to correct axial and coronal eye motion in 3D OCT
  retinal imaging
Yiqian Wang Alexandra Warter Melina Cavichini Varsha Alex Dirk-Uwe G. Bartsch William R. Freeman Truong Q. Nguyen Cheolhong An
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.