A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images

Single particle tracking is an important image analysis technique widely used in biomedical sciences to follow the movement of subcellular structures, which typically appear as individual particles in fluorescence microscopy images. In practice, the low signal-to-noise ratio (SNR) of fluorescence microscopy images as well as the high density and complex movement of subcellular structures pose substantial technical challenges for accurate and robust tracking. In this paper, we propose a novel Transformer-based single particle tracking method called Motion Transformer Tracker (MoTT). By using its attention mechanism to learn complex particle behaviors from past and hypothetical future tracklets (i.e., fragments of trajectories), MoTT estimates the matching probabilities between each live/established tracklet and its multiple hypothesis tracklets simultaneously, as well as the existence probability and position of each live tracklet. Global optimization is then used to find the overall best matching for all live tracklets. For those tracklets with high existence probabilities but missing detections due to e.g., low SNRs, MoTT utilizes its estimated particle positions to substitute for the missed detections, a strategy we refer to as relinking in this study. Experiments have confirmed that this strategy substantially alleviates the impact of missed detections and enhances the robustness of our tracking method. Overall, our method substantially outperforms competing state-of-the-art methods on the ISBI Particle Tracking Challenge datasets. It provides a powerful tool for studying the complex spatiotemporal behavior of subcellular structures. The source code is publicly available at https://github.com/imzhangyd/MoTT.git.