A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos (original) (raw)
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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.
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2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
Tracking of cell populations in vitro in time lapse microscopy images enables automatic high throughput spatiotemporal measurements of a range of cell cycle mechanics and dynamics. Both in clinical and academic environments, large scale cellular data analysis using such methods stands to facilitate a paradigm shift in approaches to understanding cell biology. In this paper, we present a novel approach to cell population tracking and segmentation. We employ the CONDENSATION algorithm in tandem with Fast Levels Sets and Exclusion Zones for robust tracking and pixel-accurate segmentation. The algorithm feeds its output to a lineage filter. The complete approach is validated in terms of its ability to track and identify nuclei, and by its success in detecting abnormalities in the length of mitosis.
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2010
This paper describes an automated visual tracking system combining time-lapse and end-point confocal microscopy to aid the interpretations of cell behaviors and interactions, with the focus on understanding the sprouting mechanism during angiogenesis. These multiple cells exhibit stochastic motion and are subjected to photobleaching and the images acquired are of low signal to noise ratio. Hence, following time-lapse imaging, high resolution end-point images are acquired. Our approach applies a probabilistic motion filter (a backward Kalman filtering followed by track smoothing) which incorporates end-point and all available time-lapse information in a mathematically consistent manner to obtain trajectory and phenotype information of multiple individual cells simultaneously. An extension of this algorithm, track smoothing with a Multiple Hypothesis Testing (MHT) data association, is proposed to improve association of multiple close contact and proliferating cells across images acquired from different time points to existing track trajectories. Our methodology was applied to tracking endothelial cell sprouting in three-dimensional micro-fluidic devices.
Joint Tracking of Cell Morphology and Motion
Lecture Notes in Computer Science, 2009
A new method is proposed for joint tracking of cell morphology and motion from 3D temporal cellular images. We adopt the framework of region-based active contours for segmentation, which is able to cope with objects having blurred boundaries. Motion estimation is performed by optical flow to increase the robustness and accuracy. Cell morphology and motion are modelled via a unified energy formulation and estimated iteratively searching for the minimum energy configuration. Experiments are carried out on synthetic and real cellular images to demonstrate the performance of the method.
2009
We have developed methods for segmentation and tracking of cells in time-lapse phase-contrast microscopy images. Our multi-object Bayesian algorithm detects and tracks large numbers of cells in presence of clutter and identifies cell division. To solve the data association problem, the assignment of current measurements to cell tracks, we tested various cost functions with both an optimal and a fast, suboptimal assignment algorithm. We also propose metrics to quantify cell migration properties, such as motility and directional persistence, and compared our findings of cell migration with the standard random walk model. We measured how cell populations respond to the physical stimuli presented in the environment, for example, the stiffness property of the substrate. Our analysis of hundreds of spatio-temporal cell trajectories revealed significant differences in the behavioral response of fibroblast cells to changes in hydrogel conditions. 186 978-1-4244-3993-5/09/$25.00
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.
Joint cell segmentation and tracking using cell proposals
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Time-lapse microscopy imaging has advanced rapidly in last few decades and is producing large volume of data in cell and developmental biology. This has increased the importance of automated analyses, which depend heavily on cell segmentation and tracking as these are the initial stages when computing most biologically important cell properties. In this paper, we propose a novel joint cell segmentation and tracking method for fluorescence microscopy sequences, which generates a large set of cell proposals, creates a graph representing different cell events and then iteratively finds the most probable path within this graph providing cell segmentations and tracks. We evaluate our method on three datasets from ISBI Cell Tracking Challenge and show that our greedy nonoptimal joint solution results in improved performance compared with state of the art methods.
Tracking of cells in a sequence of images using a low-dimension image representation
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
We propose a new image analysis method to segment and track cells in a growing colony. By using an intermediate low-dimension image representation yielded by a reliable over-segmentation process, we combine the advantages of two-steps methods (possibility to check intermediate results) and the power of simultaneous segmentation and tracking algorithms, which are able to use temporal redundancy to resolve segmentation ambiguities. We improve and measure the tracking performances with a notion of decision risk derived from cell motion priors. Our algorithm permits to extract the complete lineage of a growing colony during up to seven generations without requiring user interaction.
Tracking Cells and Their Lineages Via Labeled Random Finite Sets
IEEE Transactions on Signal Processing, 2021
Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide. This paper proposes novel online algorithms for jointly tracking and resolving lineages of an unknown and time-varying number of cells from time-lapse video data. Our approach involves modeling the cell ensemble as a labeled random finite set with labels representing cell identities and lineages. A spawning model is developed to take into account cell lineages and changes in cell appearance prior to division. We then derive analytic filters to propagate multi-object distributions that contain information on the current cell ensemble including their lineages. We also develop numerical implementations of the resulting multi-object filters. Experiments using simulation, synthetic cell migration video, and real time-lapse sequence, are presented to demonstrate the capability of the solutions.
Robust cell tracking in epithelial tissues through identification of maximum common subgraphs
2016
Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterising cell growth, proliferation, intercalation and apoptosis in epithelia helps us to understand how morphogenetic processes such as tissue invagination and extension are locally regulated and controlled. Accurate cell tracking requires correctly resolving cells entering or leaving the field of view between frames, cell neighbour exchanges, cell removals and cell divisions. However, current tracking methods for epithelial sheets are not robust to large morphogenetic deformations and require significant manual interventions. Here, we present a novel algorithm for epithelial cell tracking, exploiting the graph-theoretic concept of a `maximum common subgraph' to track cells between frames of a video. Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tis...