ARGUS实现快速高精度无监督细胞追踪，支持多种显微成像场景。
ARGUS: Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions
- 融合自适应检测与光流预测，逐帧匹配并修复轨迹断点
- 在多个公开数据集上达到90%以上追踪准确率，单次运行仅需5-6秒
- 无需标注数据和GPU，适合资源有限的实验室使用
定量分析细胞动态是现代生物学研究的核心，有助于理解免疫细胞互作、疾病进展及药物作用机制。由于噪声、形态变化、细胞重叠以及分裂融合等动态事件，时间序列显微成像中的自动细胞追踪仍具挑战性。本文提出ARGUS框架，实现加速、鲁棒、通用且无监督的细胞追踪解决方案。该框架结合自适应细胞检测、密集Farneback光流预测、帧间线性分配以及序列级轨迹片段重构步骤，可有效连接短时断续的轨迹。在公开的细胞追踪挑战数据集上，ARGUS检测准确率达0.905-0.971，追踪准确率达0.897-0.964，单次处理耗时仅5-6秒（3帧），总运行时间低于1分钟。结果表明，ARGUS是一个模块化、可解释的框架，无需训练数据或GPU即可适配不同成像模态与生物应用。代码已开源：https://github.com/Gitinc/argus。
原文摘要 · Abstract (English)
Background and Objective: Quantitative analysis of cell dynamics is central to modern biological research, providing critical insights into immune cell interactions, disease progression, and drug mechanisms. Automated cell tracking in time-lapse microscopy remains challenging due to noise, morphological variations, overlapping cells, and dynamic events such as divisions and fusions. Methods: We present ARGUS, a framework for Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions. ARGUS combines adaptive cell detection, dense Farneback optical-flow prediction, frame-to-frame linear assignment, and a sequence-level tracklet-refinement step that reconnects trajectory fragments across short temporal gaps. Results: On publicly available Cell Tracking Challenge datasets, ARGUS achieved detection accuracy of 0.905-0.971 and tracking accuracy of 0.897-0.964, with runtimes within 1 minute (5-6 seconds for 3 frames). Conclusions: ARGUS is a modular, interpretable framework that can be adapted to different imaging modalities and biological applications without training data or GPU infrastructure. The implementation is publicly available at https://github.com/Gitinc/argus
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