A rapid, nondestructive method for vascular network visualization

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Dec 01, 2020
Source ID
10.2144/btn-2020-0108

Entities

People

  • Aaron B Baker
  • Austin P Veith

Organizations

  • American Heart Association
  • Congressionally Directed Medical Research Programs
  • National Heart, Lung, and Blood Institute
  • National Institute of Biomedical Imaging and Bioengineering
  • University of Texas at Austin

Tags

Fields of Study

  • Physics

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks