Choi, Dong-Hee Liu, Hui-Wen Jung, Yong Hun Ahn, Jinchul Kim, Jin-A Oh, Dongwoo Jeong, Yeju Kim, Minseop Yoon, Hongjin Kang, Byengkyu Hong, Eunsol Song, Euijeong Chung, Seok 2024-01-19T10:04:22Z 2024-01-19T10:04:22Z 2023-02-17 2023-02 1473-0197 https://pubs.kist.re.kr/handle/201004/114046 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. English Royal Society of Chemistry Analyzing angiogenesis on a chip using deep learning-based image processing Article 10.1039/d2lc00983h 1 Lab on a Chip, v.23, no.3, pp.475 - 484 Lab on a Chip 23 3 475 484 N scie scopus 000919323000001 2-s2.0-85147171147 Biochemical Research Methods Chemistry, Multidisciplinary Chemistry, Analytical Nanoscience & Nanotechnology Instruments & Instrumentation Biochemistry & Molecular Biology Chemistry Science & Technology - Other Topics Instruments & Instrumentation Article CELL MECHANISMS DISEASE