开发自动显微镜图像跟踪和分析系统。

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Biotechnology Progress Pub Date : 2024-06-18 DOI:10.1002/btpr.3490
Lillian McAfee, Zach Heath, William Anderson, Marvin Hozi, John Walker Orr, Youngbok Abraham Kang
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引用次数: 0

摘要

显微图像分析在理解细胞行为和揭示各种生物与医学研究领域的重要见解方面发挥着至关重要的作用。在延时显微镜图像中跟踪细胞是研究细胞动态、相互作用和迁移的基本技术。虽然人工跟踪细胞是可行的,但耗时长,而且容易出现主观偏差,影响研究结果。为了解决这个问题,我们试图创建一个名为细胞分析器的自动软件解决方案,它能在显微镜图像中跟踪细胞,而用户只需输入最少的信息。细胞分析器的程序是用 Python 编写的,使用的是开源计算机视觉(OpenCV)库,具有图形用户界面,便于用户访问。所有代码的功能都经过了近似度、面积、中心点、对比度、方差和细胞跟踪测试的验证。细胞分析仪主要利用图像预处理和边缘检测技术来分离细胞边界,以便进行检测和分析。它能独特地记录检测到的细胞对象的面积、位移、速度、大小和方向,并自动将收集到的数据可视化,以便进行快速分析。我们的细胞分析仪通过图形用户界面提供了一种易于使用的工具,用于跟踪细胞运动和分析定量细胞图像。
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The development of an automated microscope image tracking and analysis system.

Microscopy image analysis plays a crucial role in understanding cellular behavior and uncovering important insights in various biological and medical research domains. Tracking cells within the time-lapse microscopy images is a fundamental technique that enables the study of cell dynamics, interactions, and migration. While manual cell tracking is possible, it is time-consuming and prone to subjective biases that impact results. In order to solve this issue, we sought to create an automated software solution, named cell analyzer, which is able to track cells within microscopy images with minimal input required from the user. The program of cell analyzer was written in Python utilizing the open source computer vision (OpenCV) library and featured a graphical user interface that makes it easy for users to access. The functions of all codes were verified through closeness, area, centroid, contrast, variance, and cell tracking test. Cell analyzer primarily utilizes image preprocessing and edge detection techniques to isolate cell boundaries for detection and analysis. It uniquely recorded the area, displacement, speed, size, and direction of detected cell objects and visualized the data collected automatically for fast analysis. Our cell analyzer provides an easy-to-use tool through a graphical user interface for tracking cell motion and analyzing quantitative cell images.

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来源期刊
Biotechnology Progress
Biotechnology Progress 工程技术-生物工程与应用微生物
CiteScore
6.50
自引率
3.40%
发文量
83
审稿时长
4 months
期刊介绍: Biotechnology Progress , an official, bimonthly publication of the American Institute of Chemical Engineers and its technological community, the Society for Biological Engineering, features peer-reviewed research articles, reviews, and descriptions of emerging techniques for the development and design of new processes, products, and devices for the biotechnology, biopharmaceutical and bioprocess industries. Widespread interest includes application of biological and engineering principles in fields such as applied cellular physiology and metabolic engineering, biocatalysis and bioreactor design, bioseparations and downstream processing, cell culture and tissue engineering, biosensors and process control, bioinformatics and systems biology, biomaterials and artificial organs, stem cell biology and genetics, and plant biology and food science. Manuscripts concerning the design of related processes, products, or devices are also encouraged. Four types of manuscripts are printed in the Journal: Research Papers, Topical or Review Papers, Letters to the Editor, and R & D Notes.
期刊最新文献
Non-thermal plasma decontamination of microbes: a state of the art. Mechanistic model of minute virus of mice elution behavior in anion exchange chromatography purification. Comparing in silico flowsheet optimization strategies in biopharmaceutical downstream processes. General strategies for IgG-like bispecific antibody purification. Issue Information
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