Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: application to microtubule growth analysis.
Quantitative analysis of dynamic processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. Deterministic approaches, which use object detection prior to tracking, perform poorly in the case of noisy image data. We propose an improved, completely automatic tracker, built within a Bayesian probabilistic framework. It better exploits spatiotemporal information and prior knowledge than common approaches, yielding more robust tracking also in cases of photobleaching and object interaction. The tracking method was evaluated using simulated but realistic image sequences, for which ground truth was available. The results of these experiments show that the method is more accurate and robust than popular tracking methods. In addition, validation experiments were conducted with real fluorescence microscopy image data acquired for microtubule growth analysis. These demonstrate that the method yields results that are in good agreement with manual tracking performed by expert cell biologists. Our findings suggest that the method may replace laborious manual procedures.
Department of Medical Informatics, Erasmus MC-University Medical Center Rotterdam, P. O. Box 2040, 3000 CA Rotterdam, The Netherlands.
Ihor
I
Smal
Katharina
K
Draegestein
Niels
N
Galjart
Wiro
W
Niessen
Erik
E
Meijering
D000465:Algorithms; D001185:Artificial Intelligence; D049109:Cell Proliferation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D008870:Microtubules / Q000502:physiology* / Q000648:ultrastructure*; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity