DeepKymoTracker:用于准确构建高度运动细胞细胞系树的工具
Khelina Fedorchuk1, Sarah M Russell1,2,3, Kajal Zibaei1
1Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia.
PloS one
|February 10, 2025
概括
DeepKymoTracker准确地跟踪高度活跃的T淋巴细胞进行细胞系构造. 这条新管道使用种子标记器和3D CNN来防止时间缩短显微镜中的细胞身份错误.
科学领域:
- 细胞生物学 细胞生物学
- 生物物理学的生物物理.
- 生物信息学是一种生物信息学.
背景情况:
- 时隔显微镜对于构建细胞谱系树至关重要.
- 准确的细胞跟踪和身份保护对于可靠的血统分析至关重要.
- 追踪像T淋巴细胞这样的高度活性的细胞,由于形状的变化和快速移动而带来了重大挑战.
研究的目的:
- 开发一个强大的计算管道,用于准确的细胞跟踪和细分.
- 为了应对在移动细胞的时间缩短显微镜中保持细胞身份的挑战.
- 为了提高细胞谱系树结构的可靠性.
主要方法:
- 开发DeepKymoTracker,这是一个整合细胞跟踪和细分的管道.
- 使用"种子"标记器在图像之间以及跟踪/细分步骤之间传输细胞位置和身份信息.
- 采用3D卷积神经网络 (CNN) 进行整合细胞检测和随时间推移的关联.
- 在合成和实验T淋巴细胞图像数据上训练模型.
主要成果:
- 与现有的五种自动分析工具相比,DeepKymoTracker表现出优越的性能.
- 该管道有效地减少了细胞身份错误,特别是在具有挑战性的移动细胞数据集中.
- "种子"机制在维护图像序列中的细胞身份方面被证明有效.
- 该软件是用Python实现的,并且可以自由使用.
结论:
- DeepKymoTracker为移动细胞群的自动化细胞系构造提供了显著的进步.
- 该工具特别适合在悬浮中跟踪细胞,克服与运动相关的困难.
- 这种方法提高了细胞谱系分析在生物研究中的准确性和可靠性.
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