Intracellular objects tracking

The tracking of intracellular organelles and vesicles is becoming increasingly important for understanding cellular dynamics. Originally, the development of tracking algorithms was mainly pursued in other fields, e.g. aerospace/military/street surveillance. However, most of this algorithm is not directly applicable to live cell microscopy data. Here we describe the algorithms that have been successfully applied to object detection and tracking specifically in vivo and in vitro motility assays. The characteristics advantages and disadvantages of the different approaches are compared.     

Towards Active Tracking of Beating Heart Motion in the Presence of Arrhythmia for Robotic Assisted Beating Heart Surgery

In robotic assisted beating heart surgery, the control architecture for heart motion tracking has stringent requirements in terms of bandwidth of the motion that needs to be tracked. In order to achieve sufficient tracking accuracy, feed-forward control algorithms, which rely on estimations of upcoming heart motion, have been proposed in the literature. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern. In their past work, the authors have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used generalized adaptive predictors, under constant and slowly varying heart rate conditions. This paper extends these studies to the case when the heart motion statistics change abruptly and significantly, such as during arrhythmias. A feasibility study is carried out to assess the motion tracking capabilities of the adaptive algorithms in the occurrence of arrhythmia during beating heart surgery. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected in vivo and includes heart rhythm irregularities. The algorithms are tested using both simulations and bench experiments on a three degree-of-freedom robotic test bed. They are also compared with a position-plus-derivative controller as well as a receding horizon model predictive controller that employs an extended Kalman filter algorithm for predicting future heart motion.     

A Novel Tracking Algorithm via Feature Points Matching

Visual target tracking is a primary task in many computer vision applications and has been widely studied in recent years. Among all the tracking methods, the mean shift algorithm has attracted extraordinary interest and been well developed in the past decade due to its excellent performance. However, it is still challenging for the color histogram based algorithms to deal with the complex target tracking. Therefore, the algorithms based on other distinguishing features are highly required. In this paper, we propose a novel target tracking algorithm based on mean shift theory, in which a new type of image feature is introduced and utilized to find the corresponding region between the neighbor frames. The target histogram is created by clustering the features obtained in the extraction strategy. Then, the mean shift process is adopted to calculate the target location iteratively. Experimental results demonstrate that the proposed algorithm can deal with the challenging tracking situations such as: partial occlusion, illumination change, scale variations, object rotation and complex background clutter. Meanwhile, it outperforms several state-of-the-art methods.     

Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs

Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.     

Direct comparison of kinematic data collected using an electromagnetic tracking system versus a digital optical system

The purpose of this study was to quantify the dynamic accuracy of kinematics measured by a digital optical motion analysis system in a gait analysis laboratory (capture volume approximately 20m(3)) compared to a standard range direct-current electromagnetic (EM) tracking device (capture volume approximately 1m(3)). This is a subset of a larger effort to establish an appropriate marker set for the optical system to quantify upperlimb kinematics simultaneously with gait, in comparison to previous studies of isolated upperlimb movements that have employed EM tracking devices. Rigid clusters of spherical reflective markers and EM sensors were attached to a mechanical articulator that mimicked three-dimensional joint rotations, similar to the elbow. As the articulator was moved through known ranges of motion (i.e. gold standard), kinematic data were collected simultaneously using both tracking systems. Both systems were tended to underestimate the range of motion; however, the application of post hoc smoothing and least-squares correction algorithms reduced these effects. When smoothing and correction algorithms were used, the magnitude of the mean difference between the gold standard and either the EM or optical system did not exceed 2 degrees for any of the compound motions performed. This level of agreement suggests that the measurements obtained from either system are clinically comparable, provided appropriate smoothing and correction algorithms are employed.     

Quantitative comparison of algorithms for tracking single fluorescent particles

Single particle tracking has seen numerous applications in biophysics, ranging from the diffusion of proteins in cell membranes to the movement of molecular motors. A plethora of computer algorithms have been developed to monitor the sub-pixel displacement of fluorescent objects between successive video frames, and some have been claimed to have "nanometer" resolution. To date, there has been no rigorous comparison of these algorithms under realistic conditions. In this paper, we quantitatively compare specific implementations of four commonly used tracking algorithms: cross-correlation, sum-absolute difference, centroid, and direct Gaussian fit. Images of fluorescent objects ranging in size from point sources to 5 microm were computer generated with known sub-pixel displacements. Realistic noise was added and the above four algorithms were compared for accuracy and precision. We found that cross-correlation is the most accurate algorithm for large particles. However, for point sources, direct Gaussian fit to the intensity distribution is the superior algorithm in terms of both accuracy and precision, and is the most robust at low signal-to-noise. Most significantly, all four algorithms fail as the signal-to-noise ratio approaches 4. We judge direct Gaussian fit to be the best algorithm when tracking single fluorophores, where the signal-to-noise is frequently near 4.     

Tracking cells in Life Cell Imaging videos using topological alignments

Background  

With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells – many algorithms tend to recognize one cell as several cells or vice versa.     

Algorithms for energy efficient mobile object tracking in wireless sensor networks

Wireless sensor networks have found more and more applications in a variety of pervasive computing environments, in their functions as data acquisition in pervasive applications. However, how to get better performance to support data acquisition of pervasive applications over WSNs remains to be a nontrivial and challenging task. The network lifetime and application requirement are two fundamental, yet conflicting, design objectives in wireless sensor networks for tracking mobile objects. The application requirement is often correlated to the delay time within which the application can send its sensing data back to the users in tracking networks. In this paper we study the network lifetime maximization problem and the delay time minimization problem together. To make both problems tractable, we have the assumption that each sensor node keeps working since it turns on. And we formulate the network lifetime maximization problem as maximizing the number of sensor nodes who don’t turn on, and the delay time minimization problem as minimizing the routing path length, after achieving the required tracking tasks. Since we prove the problems are NP-complete and APX-complete, we propose three heuristic algorithms to solve them. And we present several experiments to show the advantages and disadvantages referring to the network lifetime and the delay time among these three algorithms on three models, random graphs, grids and hypercubes. Furthermore, we implement the distributed version of these algorithms.     

Static and dynamic error of a biplanar videoradiography system using marker-based and markerless tracking techniques

The use of biplanar videoradiography technology has become increasingly popular for evaluating joint function in vivo. Two fundamentally different methods are currently employed to reconstruct 3D bone motions captured using this technology. Marker-based tracking requires at least three radio-opaque markers to be implanted in the bone of interest. Markerless tracking makes use of algorithms designed to match 3D bone shapes to biplanar videoradiography data. In order to reliably quantify in vivo bone motion, the systematic error of these tracking techniques should be evaluated. Herein, we present new markerless tracking software that makes use of modern GPU technology, describe a versatile method for quantifying the systematic error of a biplanar videoradiography motion capture system using independent gold standard instrumentation, and evaluate the systematic error of the W.M. Keck XROMM Facility's biplanar videoradiography system using both marker-based and markerless tracking algorithms under static and dynamic motion conditions. A polycarbonate flag embedded with 12 radio-opaque markers was used to evaluate the systematic error of the marker-based tracking algorithm. Three human cadaveric bones (distal femur, distal radius, and distal ulna) were used to evaluate the systematic error of the markerless tracking algorithm. The systematic error was evaluated by comparing motions to independent gold standard instrumentation. Static motions were compared to high accuracy linear and rotary stages while dynamic motions were compared to a high accuracy angular displacement transducer. Marker-based tracking was shown to effectively track motion to within 0.1?mm and 0.1 deg under static and dynamic conditions. Furthermore, the presented results indicate that markerless tracking can be used to effectively track rapid bone motions to within 0.15 deg for the distal aspects of the femur, radius, and ulna. Both marker-based and markerless tracking techniques were in excellent agreement with the gold standard instrumentation for both static and dynamic testing protocols. Future research will employ these techniques to quantify in vivo joint motion for high-speed upper and lower extremity impacts such as jumping, landing, and hammering.     

The automated analytical electrophoresis microscope

The components of an automated computer-controlled analytical electrophoresis microscope (AEMS) are described. Computer tracking of migrating cells projected under phase contrast onto a vidicon permits the rapid taking of multiple velocity measurements per cell so that reliable determinations of electrophoretic mobilities thereby result. The computer-controlled cell search and tracking algorithms allow high speed operation so that statistically valid profiles and data bases can be collected rapidly. Initial electrophoretic mobility evaluations have been carried out on populations of lymphocytes, erythrocytes, and platelets.     

Optimization algorithm performance in determining optimal controls in human movement analyses

The objective of this study was to evaluate the performance of different multivariate optimization algorithms by solving a "tracking" problem using a forward dynamic model of pedaling. The tracking problem was defined as solving for the muscle controls (muscle stimulation onset, offset, and magnitude) that minimized the error between experimentally collected kinetic and kinematic data and the simulation results of pedaling at 90 rpm and 250 W. Three different algorithms were evaluated: a downhill simplex method, a gradient-based sequential quadratic programming algorithm, and a simulated annealing global optimization routine. The results showed that the simulated annealing algorithm performed for superior to the conventional routines by converging more rapidly and avoiding local minima.     

Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black‐browed albatrosses

Species distribution models (SDM) based on tracking data from different devices are used increasingly to explain and predict seabird distributions. However, different tracking methods provide different data resolutions, ranging from < 10 m to > 100 km. To better understand the implications of this variation, we modeled the potential distribution of black‐browed albatrosses Thalassarche melanophris from South Georgia that were simultaneously equipped with a platform terminal transmitter (PTT) (high resolution) and a global location sensor (GLS) logger (coarse resolution), and measured the overlap of the respective potential distribution for a total of nine different SDM algorithms. We found slightly better model fits for the PTT than for GLS data (AUC values 0.958 ± 0.048 vs 0.95 ± 0.05) across all algorithms. The overlaps of the predicted distributions were higher between device types for the same algorithm, than among algorithms for either device type. Uncertainty arising from coarse‐resolution location data is therefore lower than that associated with the modeling technique. Consequently, the choice of an appropriate algorithm appears to be more important than device type when applying SDMs to seabird tracking data. Despite their low accuracy, GLS data appear to be effective for analyzing the habitat preferences and distribution patterns of pelagic species.     

Prediction methods for synchronization of scanned ion beam tracking

Beam tracking as a mitigation technique for treatment of intra-fractionally moving organs requires prediction to overcome latencies in the adaptation process. We implemented and experimentally tested a prediction method for scanned carbon beam tracking. Beam tracking parameters, i.e. the shift of the Bragg peak position in 3D, are determined prior to treatment in 4D treatment planning and applied during treatment delivery in dependence on the motion state of the target as well as on the scanning spot in the target. Hence, prediction is required for the organ motion trajectory as well as the scanning progress to achieve maximal performance. Prediction algorithms to determine beam displacements that overcome these latencies were implemented. Prediction times of 25 ms for target spot prediction were required for ～6 mm water-equivalent longitudinal beam shifts. The experimental tests proved feasibility of the implemented prediction algorithm.     

Optimal Appearance Model for Visual Tracking

Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models.     

Bayesian inference for improved single molecule fluorescence tracking

Single molecule tracking is widely used to monitor the change in position of lipids and proteins in living cells. In many experiments in which molecules are tagged with a single or small number of fluorophores, the signal/noise ratio may be limiting, the number of molecules is not known, and fluorophore blinking and photobleaching can occur. All these factors make accurate tracking over long trajectories difficult and hence there is still a pressing need to develop better algorithms to extract the maximum information from a sequence of fluorescence images. We describe here a Bayesian-based inference approach, based on a trans-dimensional sequential Monte Carlo method that utilizes both the spatial and temporal information present in the image sequences. We show, using model data, where the real trajectory of the molecule is known, that our method allows accurate tracking of molecules over long trajectories even with low signal/noise ratio and in the presence of fluorescence blinking and photobleaching. The method is then applied to real experimental data.     

Diatrack particle tracking software: Review of applications and performance evaluation

Object tracking is an instrumental tool supporting studies of cellular trafficking. There are three challenges in object tracking: the identification of targets; the precise determination of their position and boundaries; and the assembly of correct trajectories. This last challenge is particularly relevant when dealing with densely populated images with low signal‐to‐noise ratios—conditions that are often encountered in applications such as organelle tracking, virus particle tracking or single‐molecule imaging. We have developed a set of methods that can handle a wide variety of signal complexities. They are compiled into a free software package called Diatrack. Here we review its main features and utility in a range of applications, providing a survey of the dynamic imaging field together with recommendations for effective use. The performance of our framework is shown to compare favorably to a wide selection of custom‐developed algorithms, whether in terms of localization precision, processing speed or correctness of tracks.      

Exploring dynamics in living cells by tracking single particles

In the last years, significant advances in microscopy techniques and the introduction of a novel technology to label living cells with genetically encoded fluorescent proteins revolutionized the field of Cell Biology. Our understanding on cell dynamics built from snapshots on fixed specimens has evolved thanks to our actual capability to monitor in real time the evolution of processes in living cells. Among these new tools, single particle tracking techniques were developed to observe and follow individual particles. Hence, we are starting to unravel the mechanisms driving the motion of a wide variety of cellular components ranging from organelles to protein molecules by following their way through the cell. In this review, we introduce the single particle tracking technology to new users. We briefly describe the instrumentation and explain some of the algorithms commonly used to locate and track particles. Also, we present some common tools used to analyze trajectories and illustrate with some examples the applications of single particle tracking to study dynamics in living cells.     

Real-time tracking of parameters of lung mechanics: emphasis on algorithm tuning

We consider the problem of tracking rapid changes in the viscous and elastic properties of the respiratory system by using mouth flow and transpulmonary pressure data measured during mechanical ventilation. A recursive least-squares algorithm with adjustable compensator is used for online estimation of an R-C model of the breathing mechanics. Specific simulation experiments are presented to provide guidelines to select suitable values for the key variable, which controls the compromise between tracking ability and noise sensitivity. The results obtained confirm the critical role of the optimum tuning in relation to the noise level. Experimental results obtained from data measured on mechanically-ventilated dogs, in which respiratory distress syndrome was intravenously induced by oleic acid, demonstrate that the tuned algorithm is able to track appropriately both the viscous and elastic properties of lung mechanics. Parameter estimates are consistent with those obtained by standard and robust offine algorithms and their time course is in qualitative agreement with known physiopathological behaviour.     

A Highlights from MBoC Selection: Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Improvements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for data sets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background signal. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared with current methods. This algorithm, called Cega, for “find the object,” produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments.     

Distributed sensor activation algorithm for target tracking with binary sensor networks

Target tracking with wireless sensor networks (WSNs) has been a hot research topic recently. Many works have been done to improve the algorithms for localization and prediction of a moving target with smart sensors. However, the results are frequently difficult to implement because of hardware limitations. In this paper, we propose a practical distributed sensor activation algorithm (DSA²) that enables reliable tracking with the simplest binary-detection sensors. In this algorithm, all sensors in the field are activated with a probability to detect targets or sleep to save energy, the schedule of which depends on their neighbor sensors’ behaviors. Extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm. Great improvement in terms of energy-quality tradeoff and excellent robustness of the algorithm are also emphasized in the simulations.