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
School of Mechanical Engineering, Korea University, Seoul, 02841, Korea.
KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Korea.
Lab Chip. 2023 Jan 31;23(3):475-484. doi: 10.1039/d2lc00983h.
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.
血管生成，即从现有血管形成新血管，与70多种疾病有关。尽管众多研究已建立血管生成模型，但仅有少数指标可用于分析血管生成结构。在本研究中，我们开发了一种基于深度学习的图像处理流程来分析和量化血管生成。我们利用了几种图像处理算法来量化血管生成，包括基于深度学习的细胞核分割算法和图像骨架化。该方法可使用16个指标（包括长度、宽度、数量和细胞核分布）来量化和测量血管对生化梯度的变化。此外，该程序对于血管生成的三维定量分析非常高效，并且可应用于各种血管生成研究。
