Issue 3, 2023

Analyzing angiogenesis on a chip using deep learning-based image processing

Abstract

Angiogenesis, the formation of new blood vessels from existing vessels, has been associated with more than 70 diseases. Although numerous studies have established angiogenesis models, only a few indicators can be used to analyze angiogenic structures. In the present study, we developed an image-processing pipeline based on deep learning to analyze and quantify angiogenesis. We utilized several image-processing algorithms to quantify angiogenesis, including a deep learning-based cell nuclear segmentation algorithm and image skeletonization. This method could quantify and measure changes in blood vessels in response to biochemical gradients using 16 indicators, including length, width, number, and nuclear distribution. Moreover, this procedure is highly efficient for the three-dimensional quantitative analysis of angiogenesis and can be applied to diverse angiogenesis investigations.

Graphical abstract: Analyzing angiogenesis on a chip using deep learning-based image processing

Supplementary files

Article information

Article type
Paper
Submitted
21 Nhl 2022
Accepted
12 Sun 2023
First published
17 Sun 2023

Lab Chip, 2023,23, 475-484

Analyzing angiogenesis on a chip using deep learning-based image processing

D. Choi, H. Liu, Y. H. Jung, J. Ahn, J. Kim, D. Oh, Y. Jeong, M. Kim, H. Yoon, B. Kang, E. Hong, E. Song and S. Chung, Lab Chip, 2023, 23, 475 DOI: 10.1039/D2LC00983H

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