Open AccessJoint Motion Correction and 3D Segmentation with Graph-Assisted Neural Networks for Retinal OCT
Author(s) -
Yiqian Wang,
Carlo Galang,
William R. Freeman,
Truong Q. Nguyen,
Cheolhong An
Publication year - 2023
Publication title -
2022 ieee international conference on image processing (icip)
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.315
H-Index - 96
eISSN - 2381-8549
pISSN - 1522-4880
ISBN - 978-1-6654-9620-9
DOI - 10.1109/icip46576.2022.9898072
Subject(s) - computing and processing , signal processing and analysis
Optical Coherence Tomography (OCT) is a widely used noninvasive 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.