Work

Joint Motion Correction and 3D Segmentation with Graph-Assisted Neural Networks for Retinal OCT

Year: 2022

Type: conference-paper

Abstract: Optical Coherence Tomography (OCT) is a widely used non-invasive high resolution 3D imaging technique for biological tissues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT-Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D OCT information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion correction and 3D OCT layer segmentation comparing to conventional and deep-learning based 2D OCT layer segmentation.

Source: Proceedings - International Conference on Image Processing

DOI: 10.1109/icip46576.2022.9898072

Authors: Yiqian Wang, Carlo Galang, William R. Freeman, Truong Q. Nguyen, Cheolhong An

Institution: University of California San Diego

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Eye Institute