Optimized U-Net model for 3D light-sheet image segmentation of zebrafish trunk vessels

针对斑马鱼躯干血管三维光片图像分割的优化U-Net模型

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Abstract

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.

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