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.
Bibliographical noteFunding Information:
This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1901-51, the Technology Innovation Program (No. 20012378) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), and the Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MSIT) (No. 20009125). E. J. Song was supported by the Technology Innovation Program (20009853) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).
© 2023 The Royal Society of Chemistry.
ASJC Scopus subject areas
- Biomedical Engineering