radR: an open-source platform for acquiring and analysing data on biological targets observed by surveillance radar

<p>Abstract</p> <p>Background</p> <p>Radar has been used for decades to study movement of insects, birds and bats. In spite of this, there are few readily available software tools for the acquisition, storage and processing of such data. Program radR was developed to solve this problem.</p> <p>Results</p> <p>Program radR is an open source software tool for the acquisition, storage and analysis of data from marine radars operating in surveillance mode. radR takes time series data with a two-dimensional spatial component as input from some source (typically a radar digitizing card) and extracts and retains information of biological relevance (i.e. moving targets). Low-level data processing is implemented in "C" code, but user-defined functions written in the "R" statistical programming language can be called at pre-defined steps in the calculations. Output data formats are designed to allow for future inclusion of additional data items without requiring change to C code. Two brands of radar digitizing card are currently supported as data sources. We also provide an overview of the basic considerations of setting up and running a biological radar study.</p> <p>Conclusions</p> <p>Program radR provides a convenient, open source platform for the acquisition and analysis of radar data of biological targets.</p

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

Protein Tracking by CNN-Based Candidate Pruning and Two-Step Linking with Bayesian Network

Protein trafficking plays a vital role in understanding many biological processes and disease. Automated tracking of protein vesicles is challenging due to their erratic behaviour, changing appearance, and visual clutter. In this paper we present a novel tracking approach which utilizes a two-step linking process that exploits a probabilistic graphical model to predict tracklet linkage. The vesicles are initially detected with help of a candidate selection process, where the candidates are identified by a multi-scale spot enhancing filter. Subsequently, these candidates are pruned and selected by a light weight convolutional neural network. At the linking stage, the tracklets are formed based on the distance and the detection assignment which is implemented via combinatorial optimization algorithm. Each tracklet is described by a number of parameters used to evaluate the probability of tracklets connection by the inference over the Bayesian network. The tracking results are presented for confocal fluorescence microscopy data of protein trafficking in epithelial cells. The proposed method achieves a root mean square error (RMSE) of 1.39 for the vesicle localisation and of 0.7 representing the degree of track matching with ground truth. The presented method is also evaluated against the state-of-the-art “Trackmate“ framework

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers

A global sampler of single particle tracking solutions for single molecule microscopy.

The dependence on model-fitting to evaluate particle trajectories makes it difficult for single particle tracking (SPT) to resolve the heterogeneous molecular motions typical of cells. We present here a global spatiotemporal sampler for SPT solutions using a Metropolis-Hastings algorithm. The sampler does not find just the most likely solution but also assesses its likelihood and presents alternative solutions. This enables the estimation of the tracking error. Furthermore the algorithm samples the parameters that govern the tracking process and therefore does not require any tweaking by the user. We demonstrate the algorithm on synthetic and single molecule data sets. Metrics for the comparison of SPT are generalised to be applied to a SPT sampler. We illustrate using the example of the diffusion coefficient how the distribution of the tracking solutions can be propagated into a distribution of derived quantities. We also discuss the major challenges that are posed by the realisation of a SPT sampler

Adaptive Re-Segmentation Strategies for Accurate Bright Field Cell Tracking

Understanding complex interactions in cellular systems requires accurate tracking of individual cells observed in microscopic image sequence and acquired from multi-day in vitro experiments. To be effective, methods must follow each cell through the whole experimental sequence to recognize significant phenotypic transitions, such as mitosis, chemotaxis, apoptosis, and cell/cell interactions, and to detect the effect of cell treatments. However, high accuracy long-range cell tracking is difficult because the collection and detection of cells in images is error-prone, and single error in a one frame can cause a tracked cell to be lost. Detection of cells is especially difficult when using bright field microscopy images wherein the contrast difference between the cells and the background is very low. This work introduces a new method that automatically identifies and then corrects tracking errors using a combination of combinatorial registration, flow constraints, and image segmentation repair