Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging
This addresses involuntary motion artifacts that compromise retinal analysis like segmentation and angiography, but it is incremental as it builds on existing correction methods with a new neural network approach.
The paper tackled motion artifacts in 3D OCT retinal imaging by proposing a deep learning network to correct axial and coronal motion, achieving smaller error than other methods and effectively recovering retinal curvature.
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.