Tracking colliding cells in vivo microscopy

Automated in vivo Tracking of Cortical Oligodendrocytes

Oligodendrocytes exert a profound influence on neural circuits by accelerating action potential conduction, altering excitability, and providing metabolic support. As oligodendrogenesis continues in the adult brain and is essential for myelin repair, uncovering the factors that control their dynamics is necessary to understand the consequences of adaptive myelination and develop new strategies to enhance remyelination in diseases such as multiple sclerosis. Unfortunately, few methods exist for analysis of oligodendrocyte dynamics, and even fewer are suitable for in vivo investigation. Here, we describe the development of a fully automated cell tracking pipeline using convolutional neural networks (Oligo-Track) that provides rapid volumetric segmentation and tracking of thousands of cells over weeks in vivo. This system reliably replicated human analysis, outperformed traditional analytic approaches, and extracted injury and repair dynamics at multiple cortical depths, establishing that oligodendrogenesis after cuprizone-mediated demyelination is suppressed in deeper cortical layers. Volumetric data provided by this analysis revealed that oligodendrocyte soma size progressively decreases after their generation, and declines further prior to death, providing a means to predict cell age and eventual cell death from individual time points. This new CNN-based analysis pipeline offers a rapid, robust method to quantitatively analyze oligodendrocyte dynamics in vivo, which will aid in understanding how changes in these myelinating cells influence circuit function and recovery from injury and disease.

An Ant Colony Inspired Multi-Bernoulli Filter for Cell Tracking in Time-Lapse Microscopy Sequences

The analysis of the dynamic behavior of cells in time-lapse microscopy sequences requires the development of reliable and automatic tracking methods capable of estimating individual cell states and delineating the lineage trees corresponding to the tracks. In this paper, we propose a novel approach, i.e., an ant colony inspired multi-Bernoulli filter, to handle the tracking of a collection of cells within which mitosis, morphological change and erratic dynamics occur. The proposed technique treats each ant colony as an independent one in an ant society, and the existence probability of an ant colony and its density distribution approximation are derived from the individual pheromone field and the corresponding heuristic information for the approximation to the multi-Bernoulli parameters. To effectively guide ant foraging between consecutive frames, a dual prediction mechanism is proposed for the ant colony and its pheromone field. The algorithm performance is tested on challenging datasets with varying population density, frequent cell mitosis and uneven motion over time, demonstrating that the algorithm outperforms recently reported approaches.

A Novel Multiobject Tracking Approach in the Presence of Collision and Division

This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.

Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime.

Image-based characterization of thrombus formation in time-lapse DIC microscopy

The characterization of thrombus formation in time-lapse DIC microscopy is of increased interest for identifying genes which account for atherothrombosis and coronary artery diseases (CADs). In particular, we are interested in large-scale studies on zebrafish, which result in large amount of data, and require automatic processing. In this work, we present an image-based solution for the automatized extraction of parameters quantifying the temporal development of thrombotic plugs. Our system is based on the joint segmentation of thrombotic and aortic regions over time. This task is made difficult by the low contrast and the high dynamic conditions observed in vivo DIC microscopic scenes. Our key idea is to perform this segmentation by distinguishing the different motion patterns in image time series rather than by solving standard image segmentation tasks in each image frame. Thus, we are able to compensate for the poor imaging conditions. We model motion patterns by energies based on the idea of dynamic textures, and regularize the model by two prior energies on the shape of the aortic region and on the topological relationship between the thrombus and the aorta. We demonstrate the performance of our segmentation algorithm by qualitative and quantitative experiments on synthetic examples as well as on real in vivo microscopic sequences.