Open AccessOptimized U-Net model for 3D light-sheet image segmentation of zebrafish trunk vessels
Author(s) -
Jingyi Yin,
Guang Yang,
Xiaofei Qin,
Hui Li,
Linbo Wang
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.449714
Subject(s) - zebrafish , segmentation , light sheet fluorescence microscopy , image segmentation , computer science , artificial intelligence , trunk , computer vision , process (computing) , bottleneck , anatomy , intersection (aeronautics) , digital image analysis , fluorescence microscope , biology , optics , fluorescence , physics , ecology , biochemistry , engineering , gene , embedded system , aerospace engineering , operating system
The growth of zebrafish's vessels can be used as an indicator of the vascular development process and to study the biological mechanisms. The three-dimensional (3D) structures of zebrafish's trunk vessels could be imaged by state-of-art light-sheet fluorescent microscopy with high efficiency. A large amount of data was then produced. Accurate segmentation of these 3D images becomes a new bottleneck for automatic and quantitative analysis. Here, we propose a Multi-scale 3D U-Net model to perform the segmentation of trunk vessels. The segmentation accuracies of 82.3% and 83.0%, as evaluated by the IoU (Intersection over Union) parameter, were achieved for intersegmental vessels and the dorsal longitudinal anastomotic vessels respectively. The growth of zebrafish vasculature from 42-62 hours was then analyzed quantitatively.