The cell monolayer trajectory from the system state point of view

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.

2009

The tracking of individual cells in time-lapse microscopy facilitates the assessment of certain characteristics of different cell types. Since manual tracking of an adequate number of cells over a considerable number of frames is tedious and sometimes not feasible, there is a vital interest in automated methods. We present a rather minimalistic approach for the tracking of unstained cells in cell culture assays. The proposed approach comprises background subtraction, an object detection method based on discrete geometrical feature analysis together with a validation of the resulting graph-structures. The main advantage of this approach lies in its computational efficiency.

Chemical engineering transactions, 2014

Pharmacological research is continuously working on the development of new drugs. This research typically starts from the formulation of new molecules that are first investigated at the cell scale, finally is completed with clinical trials. Investigation on the cell scale requires simple, reproducible and reliable assays, able to simulate physiological conditions in the lab. A wide range of biological processes, such as angiogenesis, inflammation, tissue regeneration, tumour growth and invasion, are strongly linked to cell proliferation and migration mechanisms that govern the dynamic evolution of both individual cells and cell aggregates. In this work we present an experimental methodology for the quantitative investigation of cell dynamics in vitro by live imaging of biological soft matter. Cell motility is observed by means of a Time Lapse Microscopy workstation, consisting of a motorized video-microscope equipped with an incubating system, and quantified by image analysis techniques. We report some preliminary experimental results relative to the migration of a tumour cell line both in random condition and in presence of an external stimulus, such as a chemical concentration gradient. The ultimate goal of this research is the development of a standard assay to be used as a test for drug efficiency, suitable for routine application in the pharmaceutical research.

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.

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.

Critical Reviews in Oncology/Hematology, 2009

Computational Biology, 2015

Just as body language can reveal a person's state of well-being, dynamic changes in cell behavior and morphology can be used to monitor processes in cultured cells. This chapter discusses how CL-Quant software, a commercially available video bioinformatics tool, can be used to extract quantitative data on: (1) growth/proliferation, (2) cell and colony migration, (3) reactive oxygen species (ROS) production, and (4) neural differentiation. Protocols created using CL-Quant were used to analyze both single cells and colonies. Time-lapse experiments in which different cell types were subjected to various chemical exposures were done using Nikon BioStations. Proliferation rate was measured in human embryonic stem cell colonies by quantifying colony area (pixels) and in single cells by measuring confluency (pixels). Colony and single cell migration were studied by measuring total displacement (distance between the starting and ending points) and total distance traveled by the colonies/cells. To quantify ROS production, cells were pre-loaded with MitoSOX Red™, a mitochondrial ROS (superoxide) indicator, treated with various chemicals, then total intensity of the red fluorescence was measured in each frame. Lastly, neural stem cells were incubated in differentiation medium for 12 days, and time lapse images were collected daily. Differentiation of neural stem cells was quantified using a protocol that detects young neurons. CL-Quant software can be used to evaluate biological processes in living cells, and the protocols developed in this project can be applied to basic research and toxicological studies, or to monitor quality control in culture facilities.

Cell motility involves a number of strategies that cells use in order to seek nutrients, escape danger, and fulfill morphogenetic roles. Here we present a methodology to quantify morphological changes and their relationship with signaling events from time-lapse imaging microscopy experiments, in order to characterize physiological and pathological processes. To this aim, the stationary spatial pattern of signaling events is determined through an intracellular fluorescent probe, and it is related with the frequency and entity of morphodynamic events, which are in turn quantified through a stochastic approach: two pseudoimages are obtained from a time series of moving cells that describe the probability that a pixel belongs to the cell, and the probability that a pixel is subject to a dynamic event. The simultaneous construction of these maps permits visualization of hot spots of dynamic events, i.e., zones of formation of membrane protrusions and retractions and their relationship with the signaling events reported by the specific probe employed. The method is tested on spontaneous movement of cells, trasfected with redox-sensitive yellow fluorescent protein, in which the distribution of the hot spots and its change upon expression of constitutively active Rac (V12-Rac), is related to the distribution of oxidized spots.

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, 2009

In this paper, we address the problem of the analysis of cellular phenotype from time-lapse image sequences using object tracking algorithms and feature extraction and classification. We discusses the application of an object tracking algorithm for in the analysis of high content cell-migration time-lapse image sequence of extremely motile cells; these cells are captured at low time-resolution.. The small size of the objects and significant deformation of the object during the process renders the tracking as a non-trivial problem. To that end, the 'KDE Mean Shift', a real-time tracking solution, is adapted for our research. We illustrate that in a simulation experiment with artificial objects, with our algorithm an accuracy of over 90% can be established. Based on the tracking result, we propose several morphology and motility based measurements for the analysis of cell behaviour. Our analysis requires only initial manual interference; the majority of the processing is automated.

Nature Communications, 2023