LEARNING TO CORRECT AXIAL MOTION IN OCT FOR 3D RETINAL IMAGING

被引：0

作者：

Wang, Yiqian ^{[1
]}

Warter, Alexandra ^{[2
]}

Cavichini-Cordeiro, Melina ^{[2
]}

Freeman, William R. ^{[2
]}

Bartsch, Dirk-Uwe G. ^{[2
]}

Nguyen, Truong Q. ^{[1
]}

An, Cheolhong ^{[1
]}

机构：

[1] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA

[2] Shiley Eye Inst, Jacobs Retina Ctr, La Jolla, CA USA

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2021年

关键词：

Motion correction; optical coherence tomography; eye movement; deep learning; retinal imaging; COHERENCE; ARTIFACTS; SCANS;

D O I：

10.1109/ICIP42928.2021.9506620

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：126 / 130

页数：5

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