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

doi:10.1039/d2lc00983h

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

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

3 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 language	English
Pages (from-to)	475-484
Number of pages	10
Journal	Lab on a Chip
Volume	23
Issue number	3
DOIs	https://doi.org/10.1039/d2lc00983h
Publication status	Published - 2023 Jan 17

Bibliographical note

Funding 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).

Publisher Copyright:
© 2023 The Royal Society of Chemistry.

ASJC Scopus subject areas

Bioengineering
Biochemistry
Chemistry(all)
Biomedical Engineering

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title = "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.",

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

note = "Funding 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). Publisher Copyright: {\textcopyright} 2023 The Royal Society of Chemistry.",

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doi = "10.1039/d2lc00983h",

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T1 - Analyzing angiogenesis on a chip using deep learning-based image processing

AU - Choi, Dong Hee

AU - Liu, Hui Wen

AU - Jung, Yong Hun

AU - Ahn, Jinchul

AU - Kim, Jin A.

AU - Oh, Dongwoo

AU - Jeong, Yeju

AU - Kim, Minseop

AU - Yoon, Hongjin

AU - Kang, Byengkyu

AU - Hong, Eunsol

AU - Song, Euijeong

AU - Chung, Seok

N1 - Funding 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). Publisher Copyright: © 2023 The Royal Society of Chemistry.

PY - 2023/1/17

Y1 - 2023/1/17

N2 - 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.

AB - 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.

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

Abstract

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