ARGUS: Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions
Provides a fast, unsupervised cell tracking solution for biologists studying cell dynamics, though it is an incremental improvement over existing methods.
ARGUS achieves detection accuracy of 0.905-0.971 and tracking accuracy of 0.897-0.964 on Cell Tracking Challenge datasets, with runtimes under 1 minute (5-6 seconds for 3 frames), enabling automated cell tracking without training data or GPUs.
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