Efficient characterization of labeling uncertainty in closely-spaced targets tracking

In this paper we propose a novel solution to the labeled multi-target tracking problem. The method presented is specially effective in scenarios where the targets have once moved in close proximity. When this is the case, disregarding the labeling uncertainty present in a solution (after the targets split) may lead to a wrong decision by the end user. We take a closer look at the main cause of the labeling problem. By modeling the possible crosses between the targets, we define some relevant labeled point estimates. We extend the concept of crossing objects, which is obvious in one dimension, to scenarios where the objects move in multiple dimensions. Moreover, we provide a measure of uncertainty associated to the proposed solution to tackle the labeling problem. We develop a novel, scalable and modular framework in line with it. The proposed method is applied and analyzed on the basis of one-dimensional objects and two-dimensional objects simulation experiments

Labeling Uncertainty in Multitarget Tracking

In multitarget tracking, the problem of track labeling (assigning labels to tracks) is an ongoing research topic. The existing literature, however, lacks an appropriate measure of uncertainty related to the assigned labels that has a sound mathematical basis as well as clear practical meaning to the user. This is especially important in a situation where well separated targets move in close proximity with each other and thereafter separate again; in such a situation, it is well known that there will be confusion on target identities, also known as "mixed labeling." In this paper, we specify comprehensively the necessary assumptions for a Bayesian formulation of the multitarget tracking and labeling (MTTL) problem to be meaningful. We provide a mathematical characterization of the labeling uncertainties with clear physical interpretation. We also propose a novel labeling procedure that can be used in combination with any existing (unlabeled) MTT algorithm to obtain a Bayesian solution to the MTTL problem. One advantage of the resulting solution is that it readily provides the labeling uncertainty measures. Using the mixed labeling phenomenon in the presence of two targets as our test bed, we show with simulation results that an unlabeled multitarget sequential Monte Carlo (M-SMC) algorithm that employs sequential importance resampling (SIR) augmented with our labeling procedure performs much better than its "naive" extension, the labeled SIR M-SMC filter

A Bayesian solution to multi-target tracking problems with mixed labelling

In Multi-Target Tracking (MTT), the problem of assigning labels to tracks (track labelling) is vastly covered in literature and has been previously formulated using Bayesian recursion. However, the existing literature lacks an appropriate measure of uncertainty related to the assigned labels which has sound mathematical basis and clear practical meaning (to the user). This is especially important in a situation where targets move in close proximity with each other and thereafter separate again. Because, in such a situation it is well-known that there will be confusion on target identities, also known as “mixed labelling‿. In this paper, we provide a mathematical characterization of the labelling uncertainties present in Bayesian multi-target tracking and labelling (MTTL) problems and define measures of labelling uncertainties with clear physical interpretation. The introduced uncertainty measures can be used to find the optimal track label assignment, and evaluate track labelling performance. We also analyze in details the mixed labelling phenomenon in the presence of two targets. In addition, we propose a new Sequential Monte Carlo (SMC) algorithm, the Labelling Uncertainty Aware Particle Filter (LUA-PF), for the multi target tracking and labelling problem that can provide good estimates of the uncertainty measures. We validate this using simulation and show that the proposed method performs much better when compared with the performance of the SIR multi-target SMC filter

Situation assessment: an end-to-end process for the detection of objects of interest

International audienceIn this article, semi-automatic approaches are developed for wide area situation assessment in near-real-time. The two-step method consists of two granularity levels. The first entity assessment uses a new multi-target tracking algorithm (hybridization of GM-CPHD filter and MHT with road constraints) on GMTI data. The situation is then assessed by detecting objects of interest such as convoys with other data types (SAR, video). These detections are based on Bayesian networks and their credibilistic counterpart

Bayesian multiple extended target tracking using labelled random finite sets and splines

In this paper, we propose a technique for the joint tracking and labelling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. In particular, we developed a Poisson mixture variational Bayesian (PMVB) model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach

Quantitative super-resolution microscopy: pitfalls and strategies for image analysis

Super-resolution microscopy is an enabling technology that allows biologists to visualize cellular structures at nanometer length scales using far-field optics. To break the diffraction barrier, it is necessary to leverage the distinct molecular states of fluorescent probes. At the same time, the existence of these different molecular states and the photophysical properties of the fluorescent probes can complicate data quantification and interpretation. Here, we review the pitfalls in super-resolution data analysis that must be avoided for proper interpretation of images.Peer ReviewedPostprint (author's final draft

Studies of Single-Molecule Dynamics in Microorganisms

Fluorescence microscopy is one of the most extensively used techniques in the life sciences. Considering the non-invasive sample preparation, enabling live-cell compliant imaging, and the specific fluorescence labeling, allowing for a specific visualization of virtually any cellular compound, it is possible to localize even a single molecule in living cells. This makes modern fluorescence microscopy a powerful toolbox. In the recent decades, the development of new, "super-resolution" fluorescence microscopy techniques, which surpass the diffraction limit, revolutionized the field. Single-Molecule Localization Microscopy (SMLM) is a class of super-resolution microscopy methods and it enables resolution of down to tens of nanometers. SMLM methods like Photoactivated Localization Microscopy (PALM), (direct) Stochastic Optical Reconstruction Microscopy ((d)STORM), Ground-State Depletion followed by Individual Molecule Return (GSDIM) and Point Accumulation for Imaging in Nanoscale Topography (PAINT) have allowed to investigate both, the intracellular spatial organization of proteins and to observe their real-time dynamics at the single-molecule level in live cells. The focus of this thesis was the development of novel tools and strategies for live-cell SingleParticle Tracking PALM (sptPALM) imaging and implementing them for biological research. In the first part of this thesis, I describe the development of new Photoconvertible Fluorescent Proteins (pcFPs) which are optimized for sptPALM lowering the phototoxic damage caused by the imaging procedure. Furthermore, we show that we can utilize them together with Photoactivatable Fluorescent Proteins (paFPs) to enable multi-target labeling and read-out in a single color channel, which significantly simplifies the sample preparation and imaging routines as well as data analysis of multi-color PALM imaging of live cells. In parallel to developing new fluorescent proteins, I developed a high throughput data analysis pipeline. I have implemented this pipeline in my second project, described in the second part of this thesis, where I have investigated the protein organization and dynamics of the CRISPR-Cas antiviral defense mechanism of bacteria in vivo at a high spatiotemporal level with the sptPALM approach. I was successful to show the differences in the target search dynamics of the CRISPR effector complexes as well as of single Cas proteins for different target complementarities. I have also first data describing longer-lasting bound-times between effector complex and their potential targets in vivo, for which only in vitro data has been available till today. In summary, this thesis is a significant contribution for both, the advances of current sptPALM imaging methods, as well as for the understanding of the native behavior of CRISPR-Cas systems in vivo

Optical mapping of neuronal activity during seizures in zebrafish

Mapping neuronal activity during the onset and propagation of epileptic seizures can provide a better understanding of the mechanisms underlying this pathology and improve our approaches to the development of new drugs. Recently, zebrafish has become an important model for studying epilepsy both in basic research and in drug discovery. Here, we employed a transgenic line with pan-neuronal expression of the genetically-encoded calcium indicator GCaMP6s to measure neuronal activity in zebrafish larvae during seizures induced by pentylenetretrazole (PTZ). With this approach, we mapped neuronal activity in different areas of the larval brain, demonstrating the high sensitivity of this method to different levels of alteration, as induced by increasing PTZ concentrations, and the rescuing effect of an anti-epileptic drug. We also present simultaneous measurements of brain and locomotor activity, as well as a high-throughput assay, demonstrating that GCaMP measurements can complement behavioural assays for the detection of subclinical epileptic seizures, thus enabling future investigations on human hypomorphic mutations and more effective drug screening methods. Notably, the methodology described here can be easily applied to the study of many human neuropathologies modelled in zebrafish, allowing a simple and yet detailed investigation of brain activity alterations associated with the pathological phenotype