Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots

Abstract

We propose a method to detect and track multiple moving biological spot-like particles showing different kinds of dynamics in image sequences acquired through multidimensional fluorescence microscopy. It enables the extraction and analysis of information such as number, position, speed, movement, and diffusion phases of, e.g., endosomal particles. The method consists of several stages. After a detection stage performed by a three-dimensional (3-D) undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different biologically realistic movement types. Tracks are constructed, thereafter, by a data association algorithm based on the maximization of the likelihood of each IMM. The last stage consists of updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performances of the method are validated on synthetic image data and used to characterize the 3-D movement of endocytic vesicles containing quantum dots.

Application of the IMM-JPDA Filter to Multiple Target Tracking in Total Internal Reflection Fluorescence Microscopy Images

“…To show the efficacy of the combination of IMM and JPDA filters, we compared the performance of the proposed method (IMM-JPDA) with those of two related methods including the JPDA filter with a small acceleration dynamic model [8] and IMM filter along with a data association technique using the innovation matrix [6]. We also validate our experiments against a state-of-the-art commercial tracking software package (ImarisTrack).…”

“…Metrics used in previous works [2][3][4][5][6][7][8] can not properly represent the performance of a multi-target tracking algorithm. Recently, a metric based on optimal subpattern assignment (OSPA) has been introduced by Risitc et al [15] that captures the aforementioned aspects by a single value.…”

“…To deal with nonlinear dynamics of the biological structures, different numbers of linear dynamic models have been proposed in the literature [3,4,6]. Although, a large number of linear dynamics [4,6] may result a better estimation of these nonlinear dynamics, they are more computationally demanding. In addition, more dynamic models increase the uncertainty of the estimated state; because in the IMM filter, a mixture of weighted Gaussian posterior densities results in higher variance.…”

“…Bayesian tracking approaches are a class of tracking algorithm that have became popular for cell tracking applications in recent years [2][3][4][5][6][7][8]. These tracking methods can properly deal with the interaction between the targets and long disappearance intervals by incorporating prior knowledge of object dynamics and measurement models.…”

“…However, the main weakness of these methods is their high computation cost [3]. For this reason, Kalman filtering based methods, such as the interacting multiple model (IMM) filter, which are computationally effective, are still a popular alternative for biological applications [2,4,6]. In order to solve the measurement-to-target assignment problem, accurate multi-target tracking also requires robust data association.…”