Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images: Application to Microtubule Growth Analysis

TLDR

Quantitative analysis of dynamic cellular processes by fluorescence microscopy requires tracking many bright spots in noisy image sequences, yet deterministic detection‑prior approaches perform poorly on such data. The study proposes a fully automatic Bayesian probabilistic tracker for dynamic fluorescence microscopy. The tracker exploits spatiotemporal cues and prior knowledge within a Bayesian framework, and was evaluated on simulated realistic sequences with ground truth and on real microtubule growth microscopy data. Experiments show the tracker outperforms popular methods, aligns with expert manual tracking, and could replace laborious manual procedures.

Abstract

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.