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使用YOLO和一个增强的DeepSORT算法在显微镜图像中改进细胞检测和跟踪

Mokhaled N A Al-Hamadani^1,2,3, Richard Poroszlay¹, Gabor Szeman-Nagy⁴

¹Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary.

Sensors (Basel, Switzerland)

|July 30, 2025

概括

这项研究引入了用于检测和跟踪显微镜图像中的细胞的自动管道. 结合YOLOv8x和DeepSORT显著提高了细胞跟踪的准确性和生物研究数据的完整性.

科学领域:

生物技术是生物技术.
生物医学研究生物医学研究
细胞生物学细胞生物学

背景情况:

精确的细胞检测和跟踪显微镜图像对于生物研究至关重要.
现有的方法在维护数据连续性和防止细胞身份切换方面面临挑战.

研究的目的:

在显微镜图像系列中开发一条用于自动细胞检测和跟踪的集成管道.
通过将深度学习与强大的跟踪算法相结合,提高细胞跟踪数据的准确性和完整性.

主要方法:

使用微调的YOLOv8x模型进行初始细胞和细胞分裂检测.
集成了DeepSORT跟踪算法,以改善数据关联和减少细胞身份交换机.
在DeepSORT中使用预训练的卷积网络进行强大的多对象跟踪.

主要成果:

集成管道使用增强的DeepSORT算法实现了93.21%的召回.
原来的YOLOv8x模型实现了53.47%的召回率,突出显示了DeepSORT.的改进.
该系统证明了从图像数据集中有效提取详细的细胞过程信息.

结论:

拟议的管道提供了一种可靠的方法,用于在培养环境中近似细胞过程.
YOLOv8x和DeepSORT的组合显著提高了细胞跟踪性能和数据完整性.
这种方法解决了用于生物技术和生物医学应用的自动化细胞分析的关键挑战.

关键词:

深度排序 (DeepSORT) 算法使用了深度排序算法.在UKF中,UKF是UKF.这是一个YOLO模型.细胞检测检测细胞检测器细胞跟踪追踪细胞跟踪显微镜图像显微镜图像多个尺度的ResNet50多个尺度的ResNet50多个尺度的ResNet50多个尺度的ResNet50

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