DeepAI AI Chat
Log In Sign Up

Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging

by   Yiqian Wang, et al.

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.


page 1

page 4

page 6

page 9

page 10

page 14

page 17

page 18


Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence

Optical coherence tomography (OCT) is a non-invasive imaging technology ...

Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision

Projection map (PM) from optical coherence tomography (OCT) B-scan is an...

Retinal OCT Denoising with Pseudo-Multimodal Fusion Network

Optical coherence tomography (OCT) is a prevalent imaging technique for ...

A Generalized Motion Pattern and FCN based approach for retinal fluid detection and segmentation

SD-OCT is a non-invasive cross-sectional imaging modality used for diagn...

Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs

Optical coherence tomography angiography (OCTA) can non-invasively image...