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CELLECT:用于大规模高效的细胞跟踪的对比嵌入式学习.

Hongyu Zhou^1,2,3,4, Seonghoon Kim^1,2,5, Zhifeng Zhao^1,2

¹Department of Automation, Tsinghua University, Beijing, China.

|October 20, 2025

概括

此摘要是机器生成的。

细胞跟踪的新方法CELLECT使用对比学习来有效地分析大规模的细胞行为. 这种方法为免疫学,病理学和神经科学中的应用提供了对成像类型和物种的广泛概括.

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科学领域:

细胞生物学细胞生物学
生物信息学是一种生物信息学.
计算神经科学是一种计算神经科学.

背景情况:

对大规模细胞行为的定量分析对于理解生理病理过程至关重要.
在实际应用中,高效和高性能的细胞跟踪仍然是一个重大挑战.
现有的方法往往缺乏对各种生物系统所需的概括能力.

研究的目的:

介绍CELLECT,一种新的对比嵌入式学习方法,用于大规模和高效的细胞跟踪.
为了证明CELLECT在不同成像模式和物种中的广泛适用性和通用性.
验证CELLECT在具有挑战性的数据集上的表现,包括细胞跟踪挑战.

主要方法:

开发CELLECT,一个用于细胞结构的对比嵌入式学习框架.
在公共数据集上预训练CELLECT模型,以提高概括性.
使用先进的双光子成像来实现实时3D细胞跟踪和信号提取的CELLECT应用.

主要成果:

在单个数据集上进行预训练后,CELLECT表现出广泛的概括性,在不同的成像模式和物种中有效执行.
在小鼠淋巴结中的生殖中心形成期间,实现了大规模B细胞的实时3D跟踪.
在小鼠脏中对细胞-细菌相互作用的定量识别和在非刚性运动期间高准确度的神经信号提取成功进行.

结论:

CELLECT为大规模的细胞跟踪提供了高性能和高效的解决方案.
该方法在免疫学,病理学和神经科学中具有广泛的应用潜力.
CELLECT在数据集和成像类型中进行概括的能力代表了细胞跟踪技术的重大进步.