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ARGUS is a novel framework designed for automated cell tracking in time-lapse microscopy, addressing challenges such as noise and overlapping cells through a combination of adaptive detection and optical-flow prediction. The system achieves impressive detection and tracking accuracy, ranging from 0.905 to 0.971 and 0.897 to 0.964, respectively, while processing frames in under a minute. Its modular and unsupervised nature allows for adaptability across various imaging modalities without the need for extensive training data or GPU resources, making it a significant advancement in quantitative cell dynamics analysis.
ARGUS achieves up to 97% tracking accuracy in under a minute, revolutionizing automated cell tracking without the need for training data or GPU support.
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
