Simulation and Estimation of Fluorescence Microscopy Image Sequences for Intracellular Dynamics and Trafficking (original) (raw)
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Medical Image Analysis, 2009
Image sequence analysis in video-microscopy has now gained importance since molecular biology is presently having a profound impact on the way research is being conducted in medicine. However, image processing techniques that are currently used for modeling intracellular dynamics, are still relatively crude and yield imprecise results. Indeed, complex interactions between a large number of small moving particles in a complex scene cannot be easily modeled, limiting the performance of object detection and tracking algorithms. This motivates our present research effort which is to develop a general estimation/ simulation framework able to produce image sequences showing small moving spots in interaction, with variable velocities, and corresponding to intracellular dynamics and trafficking in biology. It is now well established that spot/object trajectories can play a role in the analysis of living cell dynamics and simulating realistic image sequences is then of major importance. We demonstrate the potential of the proposed simulation/estimation framework in experiments, and show that this approach can also be used to evaluate the performance of object detection/tracking algorithms in video-microscopy and fluorescence imagery.
Tracking Colliding Cells In Vivo Microscopy
IEEE Transactions on Biomedical Engineering, 2011
Leukocyte motion represents an important component in the innate immune response to infection. Intravital microscopy is a powerful tool as it enables in vivo imaging of leukocyte motion. Under inflammatory conditions, leukocytes may exhibit various motion behaviors, such as flowing, rolling, and adhering. With many leukocytes moving at a wide range of speeds, collisions occur. These collisions result in abrupt changes in the motion and appearance of leukocytes. Manual analysis is tedious, error prone, time consuming, and could introduce technician-related bias. Automatic tracking is also challenging due to the noise inherent in in vivo images and abrupt changes in motion and appearance due to collision. This paper presents a method to automatically track multiple cells undergoing collisions by modeling the appearance and motion for each collision state and testing collision hypotheses of possible transitions between states. The tracking results are demonstrated using in vivo intravital microscopy image sequences. We demonstrate that 1) 71% of colliding cells are correctly tracked; (2) the improvement of the proposed method is enhanced when the duration of collision increases; and (3) given good detection results, the proposed method can correctly track 88% of colliding cells. The method minimizes the tracking failures under collisions and, therefore, allows more robust analysis in the study of leukocyte behaviors responding to inflammatory conditions.
Multiple objects tracking in fluorescence microscopy
Journal of Mathematical Biology, 2009
Many processes in cell biology are connected to the movement of compact entities: intracellular vesicles and even single molecules. The tracking of individual objects is important for understanding cellular dynamics. Here we describe the tracking algorithms which have been developed in the non-biological fields and successfully applied to object detection and tracking in biological applications. The characteristics features of the different algorithms are compared.
Analysis of High-throughput Microscopy Videos: Catching Up with Cell Dynamics
Lecture Notes in Computer Science, 2015
We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.
ADAPTIVE SEGMENTATION OF CELLS AND PARTICLES IN FLUORESCENT MICROSCOPE IMAGE
Journal 4 Research - J4R Journal, 2015
Understanding the mechanisms of cell motility and their regulation is an important challenge in biomedical research. The ability of cells to exert forces on their environment and alter their shape as they move is essential to various biological processes such as the immune response, embryonic development, or tumor genesis .Recent technological advances in con-focal fluorescence microscopy have given researchers the opportunity to investigate these complex processes in vivo. However, they also lead to a tremendous increase in the amount of image data acquired during the studies. Therefore, the analysis of time-lapse experiments relies increasingly on automated image processing techniques. Namely, there is a high demand for fast and robust methods to help biologists to quantitatively analyze time-lapse image data. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively. The crucial tasks are, in particular, segmenting, tracking, and evaluating movement tracks and morphological changes of cells, sub-cellular components and other particles.
Automatic Analysis of Moving Particles by Total Internal Reflection Fluorescence Microscopy
Communications in Computer and Information Science, 2019
Using of TIRF microscopy videosequences allows to study the propagation of intracellular protein vesicles to the cell surface upon insulin stimulation with high spatio-temporal resolution. Traditional tracking algorithms make mistakes for overlapping points at the intersection trajectories. Therefore, a particle registration algorithm based on background correction has shown optimal results for detecting moving particles or particles of variable size. In this paper we test traditional tracking methods and check performance for tracking protein particle in cell from video sequence TIRFM microscopy. We test four algorithms for detection vesicles location: Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), Determinant of Hessian and algorithm on base continues brightness analysis. In our opinion for determination of position of protein vesicles the best algorithm is based on continues brightness analysis. There are many algorithms for tracking single objects and multitracking algorithms based on them. We used a function that takes a tracker type as input and creates a tracker object. We analyzed seven different multitracking algorithms with realization in OpenCV (BOOSTING, MIL, KCF, TLD, MEDIANFLOW, MOSSE, CSRT) and additional tracker on base of optical flow. In such tests, MEDIANFLOW tracker gives best results for predictable motion and small displacements. This tracker gives an error message in case of incorrect tracking, unlike other trackers that continue to work even the tracking failed.
Stochastic geometry for multiple object tracking in fluorescence microscopy
2016
This paper proposes a framework for tracking multiple fluorescent objects in 2D + time video-microscopy. We present a novel batch-processing track-before-detect multiple object tracking approach based on a spatio-temporal marked point process model of ellipses. Our approach takes into account events such as births, deaths, splits and merges of objects which are motivated by the biological and physical considerations. We show the performance of the proposed model on synthetic biological data and a real total internal reflection fluorescence microscopy (TIRF) image sequence.
Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, 2016
Several major advances in Cell and Molecular Biology have been made possible by recent advances in livecell microscopy imaging. To support these efforts, automated image analysis methods such as cell segmentation and tracking during a time-series analysis are needed. To this aim, one important step is the validation of such image processing methods. Ideally, the "ground truth" should be known, which is possible only by manually labelling images or in artificially produced images. To simulate artificial images, we have developed a platform for simulating biologically inspired objects, which generates bodies with various morphologies and kinetics and, that can aggregate to form clusters. Using this platform, we tested and compared four tracking algorithms: Simple Nearest-Neighbour (NN), NN with Morphology and two DBSCAN-based methods. We show that Simple NN works well for small object velocities, while the others perform better on higher velocities and when clustering occurs. Our new platform for generating new benchmark images to test image analysis algorithms is openly available at (http://griduni.uninova.pt/Clustergen/ClusterGen\_v1.0.zip).
IEEE Transactions on Biomedical Engineering, 2000
This paper presents a vision-based method for automatic tracking of biological cells in time-lapse microscopy by combining the motion features with the topological features of the cells. The automation of tracking frequently faces problems of segmentation error and of finding correct cell correspondence in consecutive frames, since the cells are of varying size and shape, and may have uneven movement; these problems become more acute when the cell population is very high. To reduce the segmentation error, we introduce a cell-detection method based on h-maxima transformation, followed by the fitting of an ellipse for the nucleus shape. To find the correct correspondence between the detected cells, the topological features, namely, color compatibility, area overlap and deformation are combined with the motion features of skewness and displacement. This reduces the ambiguity of matching and constructs accurately the trajectories of the cell proliferation. Finally, a template-matching-based backward tracking procedure is employed to recover any break in a cell trajectory that may occur due to the segmentation errors or the presence of a mitosis. The tracking procedure is tested using a number of different cell sequences with nonuniform illumination, or uneven cell motion, and is shown to provide high accuracy both in the detection and the tracking of the cells.
Journal of Microscopy, 2009
Analysis of in vitro cell motility is a useful tool for assessing cellular response to a range of factors. However, the majority of cell-tracking systems available are designed primarily for use with fluorescently labelled images. In this paper, five commonly used tracking systems are examined for their performance compared with the use of a novel in-house celltracking system based on the principles of image registration and optical flow. Image registration is a tool commonly used in medical imaging to correct for the effects of patient motion during imaging procedures and works well on low-contrast images, such as those found in bright-field and phase-contrast microscopy. The five cell-tracking systems examined were Retrac, a manual tracking system used as the gold standard; CellTrack, a recently released freely downloadable software system that uses a combination of tracking methods; ImageJ, which is a freely available piece of software with a plug-in for automated tracking (MTrack2) and Imaris and Volocity, both commercially available automated tracking systems. All systems were used to track migration of human epithelial cells over ten frames of a phase-contrast time-lapse microscopy sequence. This showed that the in-house image-registration system was the most effective of those tested when tracking non-dividing epithelial cells in low-contrast images, with a successful tracking rate of 95%. The performance of the tracking systems was also evaluated by tracking fluorescently labelled epithelial cells imaged with both phase-contrast and confocal microscopy techniques. The results showed that using fluorescence microscopy instead of phase contrast does improve the tracking efficiency for each of the tested systems. For the in-house software, this improvement was relatively small (<5% difference in tracking success rate), whereas much greater improvements in performance were seen when using fluorescence microscopy with Volocity and ImageJ.