Cell population tracking and lineage construction with spatiotemporal context

Abstract

Automated visual-tracking of cell populations in vitro using time-lapse phase contrast microscopy enables quantitative, systematic, and high-throughput measurements of cell behaviors. These measurements include the spatiotemporal quantification of cell migration, mitosis, apoptosis, and the reconstruction of cell lineages. The combination of low signal-to-noise ratio of phase contrast microscopy images, high and varying densities of the cell cultures, topological complexities of cell shapes, and wide range of cell behaviors poses many challenges to existing tracking techniques. This paper presents a fully automated multi-target tracking system that can efficiently cope with these challenges while simultaneously tracking and analyzing thousands of cells observed using time-lapse phase contrast microscopy. The system combines bottom-up and top-down image analysis by integrating multiple collaborative modules, which exploit a fast geometric active contour tracker in conjunction with adaptive interacting multiple models (IMM) motion filtering and spatiotemporal trajectory optimization. The system, which was tested using a variety of cell populations, achieved tracking accuracy in the range of 86.9-92.5%.

Cell cycle dependent TN‐C promoter activity determined by live cell imaging

“…Understanding precisely the role of a gene product, and how its expression is regulated, requires understanding the temporal characteristics of expression, the stochastic fluctuations of that expression, and the influences of processes such as cell growth and cell cycling. Although it is relatively easy to collect a dataset of large numbers of sequential images of live cells on commercial instrumentation, deriving reliable and quantitative data from large numbers of migrating and proliferating cells over long periods of time remains challenging (36,37). The cell segmentation and tracking analysis performed here was based on the phase contrast channel alone.…”

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

“…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.…”

Phase Contrast Image Restoration via Dictionary Representation of Diffraction Patterns

“…The common techniques employed for cell segmentation include thresholding [2], edge detection, and morphological operations [3]. These methods often fail when the contrast between cells and background is low.…”