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Learning to Correct Axial Motion in OCT For 3D Retinal Imaging

Citation Author(s):
Yiqian Wang, Alexandra Warter, Melina Cavichini-Cordeiro, William R. Freeman, Dirk-Uwe G. Bartsch, Truong Q. Nguyen, Cheolhong An
Submitted by:
Yiqian Wang
Last updated:
5 October 2021 - 2:56pm
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Yiqian Wang
Paper Code:
1469

Abstract 

Abstract: 

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

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