用于视网膜光学相干断层扫描的基于图辅助神经网络的关节运动校正和三维分割
JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT.
作者信息
Wang Yiqian, Galang Carlo, Freeman William R, Nguyen Truong Q, An Cheolhong
机构信息
Department of Electrical and Computer Engineering, University of California, San Diego.
Jacobs Retina Center, Shiley Eye Institute, University of California, San Diego.
出版信息
Proc Int Conf Image Proc. 2022 Oct;2022:766-770. doi: 10.1109/icip46576.2022.9898072. Epub 2022 Oct 18.
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.
光学相干断层扫描（OCT）是一种广泛应用于生物组织的非侵入性高分辨率三维成像技术，在眼科领域发挥着重要作用。OCT视网膜层分割是OCT血管造影投影和疾病分析的基本图像处理步骤。视网膜成像中的一个主要问题是不自主眼球运动引入的运动伪影。在本文中，我们提出了利用三维OCT信息联合校正眼球运动和视网膜层分割的神经网络，以使相邻B扫描之间的分割保持一致。实验结果表明，与传统的基于深度学习的二维OCT层分割相比，结合运动校正和三维OCT层分割在视觉和定量方面都有改进。
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