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Scientific Publication Citing Dragonfly

A rapid, nondestructive method for vascular network visualization

Austin Veith (1), Aaron B Baker (1)
BioTechniques, 69, October 2020: 443–449. DOI: 10.2144/btn-2020-0108

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Keywords

blood vessels, convolutional neural networks, imaging, microCT, microvasculature, nondestructive, vasculature, visualization

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Abstract

The quantitative analysis of blood vessel networks is an important component in many animal models of disease. We describe a nondestructive technique for blood vessel imaging that visualizes in situ vasculature in harvested tissues. The method allows for further analysis of the same tissues with histology and other methods that can be performed on fixed tissue. Consequently, it can easily be incorporated upstream to analysis methods to augment these with a three-dimensional reconstruction of the vascular network in the tissues to be analyzed. The method combines iodine-enhanced micro-computed tomography with a deep learning algorithm to segment vasculature within tissues. The procedure is relatively simple and can provide insight into complex changes in the vascular structure in the tissues.

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How Our Software Was Used

Dragonfly was used to perform image segmentation, visualization, and analysis.

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Author Affiliation

(1) University of Texas at Austin, Austin, TX, USA.

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