PPF - A Parallel Particle Filtering Library

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributed-memory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with the necessary tools for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency. To the best of our knowledge, the PPF library is the first open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data Fusion & Target Tracking Conference 201

PLANT: A Method for Detecting Changes of Slope in Noisy Trajectories

Time traces obtained from a variety of biophysical experiments contain valuable information on underlying processes occurring at the molecular level. Accurate quantification of these data can help explain the details of the complex dynamics of biological systems. Here, we describe PLANT (Piecewise Linear Approximation of Noisy Trajectories), a segmentation algorithm that allows the reconstruction of time-trace data with constant noise as consecutive straight lines, from which changes of slopes and their respective durations can be extracted. We present a general description of the algorithm and perform extensive simulations to characterize its strengths and limitations, providing a rationale for the performance of the algorithm in the different conditions tested. We further apply the algorithm to experimental data obtained from tracking the centroid position of lymphocytes migrating under the effect of a laminar flow and from single myosin molecules interacting with actin in a dual-trap force-clamp configuration.The authors gratefully acknowledge financial support fromthe European Commission (FP7-ICT-2011-7, grant number 288263), Erasmus Mundus Doctorate Program Europhoton-ics (grant number 159224-1-2009-1-FR-ERA MUNDUS-EMJD), Spanish Ministry of Economy and Competi-tiveness (“Severo Ochoa” Programme for Centres of Excellence in Research & Development SEV-2015-0522,and FIS2014-56107-R grants), Generalitat de Catalunyathrough the CERCA program, Italian Ministry of Uni-versity and Research (FIRB “Futuro in Ricerca” 2013grant n. RBFR13V4M2 and Flagship Project NANOMAX),Fundaci ́o Privada CELLEX (Barcelona), Ente Cassa diRisparmio di Firenze, Human Frontier Science Program (GARGP0027/2012) and LaserLab Europe 4 (GA 654148). C.M.acknowledges funding from the Spanish Ministry of Econ-omy and Competitiveness (MINECO) and the EuropeanSocial Fund (ESF) through the Ram ́on y Cajal program 2015(RYC-2015-17896).Peer ReviewedPostprint (author's final draft

Piecewise-stationary motion modeling and iterative smoothing to track heterogeneous particle motions in dense environments

International audienceOne of the major challenges in multiple particle tracking is the capture of extremely heterogeneous movements of objects in crowded scenes. The presence of numerous assignment candidates in the expected range of particle motion makes the tracking ambiguous and induces false positives. Lowering the ambiguity by reducing the search range, on the other hand, is not an option, as this would increase the rate of false negatives. We propose here a piecewise-stationary motion model (PMM) for the particle transport along an iterative smoother that exploits recursive tracking in multiple rounds in forward and backward temporal directions. By fusing past and future information, our method, termed PMMS, can recover fast transitions from freely or confined diffusive to directed motions with linear time complexity. To avoid false positives we complemented recursive tracking with a robust inline estimator of the search radius for assignment (a.k.a. gating), where past and future information are exploited using only two frames at each optimization step. We demonstrate the improvement of our technique on simulated data – especially the impact of density, variation in frame to frame displacements, and motion switching probability. We evaluated our technique on the 2D particle tracking challenge dataset published by Chenouard et al in 2014. Using high SNR to focus on motion modeling challenges, we show superior performance at high particle density. On biological applications, our algorithm allows us to quantify the extremely small percentage of motor-driven movements of fluorescent particles along microtubules in a dense field of unbound, diffusing particles. We also show with virus imaging that our algorithm can cope with a strong reduction in recording frame rate while keeping the same performance relative to methods relying on fast sampling

Local characterization of hindered Brownian motion by using digital video microscopy and 3D particle tracking

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.In this article we present methods for measuring hindered Brownian motion in the confinement of complex 3D geometries using digital video microscopy. Here we discuss essential features of automated 3D particle tracking as well as diffusion data analysis. By introducing local mean squared displacement-vs-time curves, we are able to simultaneously measure the spatial dependence of diffusion coefficients, tracking accuracies and drift velocities. Such local measurements allow a more detailed and appropriate description of strongly heterogeneous systems as opposed to global measurements. Finite size effects of the tracking region on measuring mean squared displacements are also discussed. The use of these methods was crucial for the measurement of the diffusive behavior of spherical polystyrene particles (505 nm diameter) in a microfluidic chip. The particles explored an array of parallel channels with different cross sections as well as the bulk reservoirs. For this experiment we present the measurement of local tracking accuracies in all three axial directions as well as the diffusivity parallel to the channel axis while we observed no significant flow but purely Brownian motion. Finally, the presented algorithm is suitable also for tracking of fluorescently labeled particles and particles driven by an external force, e.g., electrokinetic or dielectrophoretic forces.S.L.D. acknowledges funding from the German Academic Exchange Service (DAAD) and the German National Academic Foundation. S.P. and U.F.K. were supported by an ERC starting grant. S.P. also acknowledges the support from the Leverhulme Trust and the Newton Trust through an Early Career Fellowship

Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem