Cross-Domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography

Yu，Shuai1,6; Liu，Yonghuai2; Zhang，Jiong3; Xie，Jianyang1; Zheng，Yalin4; Liu，Jiang5; Zhao，Yitian1

10.1007/978-3-030-87237-3_2

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

12908

13-23

Optical Coherence Tomography Angiography (OCTA) has been widely used by ophthalmologists for decision-making due to its superiority in providing caplillary details. Many of the OCTA imaging devices used in clinic provide high-quality 2D en face representations, while their 3D data quality are largely limited by low signal-to-noise ratio and strong projection artifacts, which restrict the performance of depth-resolved 3D analysis. In this paper, we propose a novel 2D-to-3D vessel reconstruction framework based on the 2D en face OCTA images. This framework takes advantage of the detailed 2D OCTA depth map for prediction and thus does not rely on any 3D volumetric data. Based on the data with available vessel depth labels, we first introduce a network with structure constraint blocks to estimate the depth map of blood vessels in other cross-domain en face OCTA data with unavailable labels. Afterwards, a depth adversarial adaptation module is proposed for better unsupervised cross-domain training, since images captured using different devices may suffer from varying image contrast and noise levels. Finally, vessels are reconstructed in 3D space by utilizing the estimated depth map and 2D vascular information. Experimental results demonstrate the effectiveness of our method and its potential to guide subsequent vascular analysis in 3D domain.

3D vessel reconstruction Domain adaptation OCTA

其他

英语

CPCI-S ; EI

Acoustics ; Computer Science ; Engineering ; General & Internal Medicine ; Microscopy ; Radiology, Nuclear Medicine & Medical Imaging

Acoustics ; Computer Science, Artificial Intelligence ; Engineering, Biomedical ; Medicine, General & Internal ; Microscopy ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000712019200002

20214110994559

Angiography ; Blood vessels ; Decision making ; Image reconstruction ; Medical computing ; Quality control ; Signal to noise ratio ; Volumetric analysis

Biomedical Engineering:461.1 ; Biological Materials and Tissue Engineering:461.2 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Computer Applications:723.5 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746 ; Chemistry:801 ; Management:912.2 ; Quality Assurance and Control:913.3

2-s2.0-85116501118

Scopus

1.Cixi Institute of Biomedical Engineering,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo,China
2.Department of Computer Science,Edge Hill University,Ormskirk,United Kingdom
3.Keck School of Medicine,University of Southern California,Los Angeles,United States
4.Department of Eye and Vision Science,University of Liverpool,Liverpool,United Kingdom
5.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
6.University of Chinese Academy of Sciences,Beijing,China

Yu，Shuai,Liu，Yonghuai,Zhang，Jiong,et al. Cross-Domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography[C],2021:13-23.