ARGUS: Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions

2026-07-09

AI Summary

Cell tracking in time-lapse microscopy images is challenged by noise, morphological variability, and complex events such as cell division and fusion. This work proposes ARGUS, a training-free and GPU-independent framework that integrates adaptive cell detection, dense Farneback optical flow, inter-frame linear assignment, and trajectory fragment reconnection to achieve robust, modular, and interpretable tracking. Designed for broad applicability across imaging modalities, ARGUS demonstrates strong performance on the Cell Tracking Challenge benchmark, attaining detection accuracies of 0.905–0.971 and tracking accuracies of 0.897–0.964, while processing three frames in only 5–6 seconds.

Abstract

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

Problem

• cell tracking
• time-lapse microscopy
• noise
• morphological variations
• dynamic events

Innovation

• unsupervised cell tracking
• optical flow
• linear assignment
• tracklet refinement
• adaptive detection