PUBLICATION
Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network
- Authors
- Chen, C., Tang, Y., Tan, Y., Wang, L., Li, H.
- ID
- ZDB-PUB-230307-34
- Date
- 2023
- Source
- Biomedical Optics Express 14: 971984971-984 (Journal)
- Registered Authors
- Keywords
- none
- MeSH Terms
- none
- PubMed
- 36874479 Full text @ Biomed. Opt. Express
Citation
Chen, C., Tang, Y., Tan, Y., Wang, L., Li, H. (2023) Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network. Biomedical Optics Express. 14:971984971-984.
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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping