Biological studies of intracellular dynamic processes commonly require motion analysis of large numbers of particles in live-cell time-lapse fluorescence microscopy imaging data. Many particle tracking methods have been developed in the past years as a first step toward fully automating this task and enabling high-throughput data processing. Two crucial aspects of any particle tracking method are the detection of relevant particles in the image frames and their linking or association from frame to frame to reconstruct the trajectories. The performance of detection techniques as well as specific combinations of detection and linking techniques for particle tracking have been extensively evaluated in recent studies. Comprehensive evaluations of linking techniques per se, on the other hand, are lacking in the literature. Here we present the results of a quantitative comparison of data association techniques for solving the linking problem in biological particle tracking applications. Nine multiframe and two more traditional two-frame techniques are evaluated as a function of the level of missing and spurious detections in various scenarios. The results indicate that linking techniques are generally more negatively affected by missing detections than by spurious detections. If misdetections can be avoided, there appears to be no need to use sophisticated multiframe linking techniques. However, in the practically likely case of imperfect detections, the latter are a safer choice. Our study provides users and developers with novel information to select the right linking technique for their applications, given a detection technique of known quality.

Additional Metadata
Keywords Correspondence problem, Fluorescence microscopy, Lagrangian relaxation, Multiframe data association, Object tracking
Persistent URL dx.doi.org/10.1016/j.media.2015.06.006, hdl.handle.net/1765/89572
Journal Medical Image Analysis
Citation
Smal, I, & Meijering, E. (2015). Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy. Medical Image Analysis, 24(1), 163–189. doi:10.1016/j.media.2015.06.006