Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network

Chong Chen; YuJun Tang; Yao Tan; LinBo Wang; Hui Li

doi:10.1364/BOE.484351

Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network

Biomed Opt Express. 2023 Jan 31;14(2):971-984. doi: 10.1364/BOE.484351. eCollection 2023 Feb 1.

Authors

Chong Chen^{1

2}, YuJun Tang^{1

2}, Yao Tan², LinBo Wang², Hui Li²

Affiliations

¹ School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 230041, China.
² Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China.

Abstract

Quantitative analysis of zebrafish cerebral vasculature is essential for the study of vascular development and disease. We developed a method to accurately extract the cerebral vasculature topological parameters of transgenic zebrafish embryos. The intermittent and hollow vascular structures of transgenic zebrafish embryos, obtained from 3D light-sheet imaging, were transformed into continuous solid structures with a filling-enhancement deep learning network. The enhancement enables the extraction of 8 vascular topological parameters accurately. Quantitation of the zebrafish cerebral vasculature vessels with the topological parameters show a developmental pattern transition from 2.5 to 5.5 dpf.