基于填充增强深度学习网络的转基因斑马鱼胚胎三维脑血管系统拓扑参数提取
Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network.
作者信息
Chen Chong, Tang YuJun, Tan Yao, Wang LinBo, Li Hui
机构信息
School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 230041, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China.
出版信息
Biomed Opt Express. 2023 Jan 31;14(2):971-984. doi: 10.1364/BOE.484351. eCollection 2023 Feb 1.
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.
斑马鱼脑血管系统的定量分析对于血管发育和疾病的研究至关重要。我们开发了一种方法来准确提取转基因斑马鱼胚胎的脑血管系统拓扑参数。通过3D光片成像获得的转基因斑马鱼胚胎的间歇性和中空血管结构，利用填充增强深度学习网络将其转化为连续的实体结构。这种增强使得能够准确提取8个血管拓扑参数。利用这些拓扑参数对斑马鱼脑血管系统血管进行定量分析，结果显示在受精后2.5至5.5天出现发育模式转变。
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