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

Dong Hee Choi, Hui Wen Liu, Yong Hun Jung, Jinchul Ahn, Jin A. Kim, Dongwoo Oh, Yeju Jeong, Minseop Kim, Hongjin Yoon, Byengkyu Kang, Eunsol Hong, Euijeong Song, Seok Chung

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)475-484
Number of pages10
JournalLab on a Chip
Volume23
Issue number3
DOIs
Publication statusPublished - 2023 Jan 17

Bibliographical note

Publisher Copyright:
© 2023 The Royal Society of Chemistry.

ASJC Scopus subject areas

  • Bioengineering
  • Biochemistry
  • General Chemistry
  • Biomedical Engineering

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