Globally Optimal Cell Tracking using Integer Programming

We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor

Cellular tracking in time-lapse phase contrast images

The quantitative analysis of live cells is a key issue in evaluating biological processes. The current clinical practice involves the application of a tedious and time consuming manual tracking procedure on large amount of data. As a result, automatic tracking systems are currently developed and evaluated. However, problems caused by cellular division, agglomeration, Brownian motion and topology changes are difficult issues that have to be accommodated by automatic tracking techniques. In this paper, we detail the development of a fully automated multi-target tracking system that is able to deal with Brownian motion and cellular division. During the tracking process our approach includes the neighbourhood relationship and motion history to enforce the cellular tracking continuity in the spatial and temporal domain. The experimental results reported in this paper indicate that our method is able to accurately track cellular structures in time-lapse data

Tracking cells in Life Cell Imaging videos using topological alignments

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

A novel framework for cellular tracking and mitosis detection in dense phase contrast microscopy images

The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by non-structured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the inter-frame cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy

An end-to-end software solution for the analysis of high-throughput single-cell migration data

The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory-and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their highthroughput data processing

Phase transitions during fruiting body formation in Myxococcus xanthus

The formation of a collectively moving group benefits individuals within a population in a variety of ways such as ultra-sensitivity to perturbation, collective modes of feeding, and protection from environmental stress. While some collective groups use a single organizing principle, others can dynamically shift the behavior of the group by modifying the interaction rules at the individual level. The surface-dwelling bacterium Myxococcus xanthus forms dynamic collective groups both to feed on prey and to aggregate during times of starvation. The latter behavior, termed fruiting-body formation, involves a complex, coordinated series of density changes that ultimately lead to three-dimensional aggregates comprising hundreds of thousands of cells and spores. This multi-step developmental process most likely involves several different single-celled behaviors as the population condenses from a loose, two-dimensional sheet to a three-dimensional mound. Here, we use high-resolution microscopy and computer vision software to spatiotemporally track the motion of thousands of individuals during the initial stages of fruiting body formation. We find that a combination of cell-contact-mediated alignment and internal timing mechanisms drive a phase transition from exploratory flocking, in which cell groups move rapidly and coherently over long distances, to a reversal-mediated localization into streams, which act as slow-spreading, quasi-one-dimensional nematic fluids. These observations lead us to an active liquid crystal description of the myxobacterial development cycle.Comment: 16 pages, 5 figure

High-Resolution Quantification of Focal Adhesion Spatiotemporal Dynamics in Living Cells

Focal adhesions (FAs) are macromolecular complexes that provide a linkage between the cell and its external environment. In a motile cell, focal adhesions change size and position to govern cell migration, through the dynamic processes of assembly and disassembly. To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones. As a proof-of-concept, we show how a single point mutation in Paxillin at the Jun-kinase phosphorylation site Serine 178 changes FA size, distribution, and rate of assembly. This study provides a detailed, quantitative picture of FA spatiotemporal dynamics as well as a set of tools and methodologies for advancing our understanding of how focal adhesions are dynamically regulated in living cells. A full, open-source software implementation of this pipeline is provided at http://gomezlab.bme.unc.edu/tools

Computational Analysis of Dancing Cells

Collective cell migration is observed during morphogenesis, angiogenesis, and wound healing, and this type of cell migration also contributes to efficient metastasis in some kinds of cancers. Because collectively migrating cells are much better organized than a random assemblage of individual cells, there seems to be a kind of order in migrating clusters. Extensive research has identified a large number of molecules involved in collective cell migration, and these factors have been analyzed using dramatic advances in imaging technology. To date, however, it remains unclear how myriad cells are integrated as a single unit. Recently, we observed unbalanced collective cell migrations that can be likened to either precision dancing or awa-odori, Japanese traditional dancing similar to the style at Rio Carnival, caused by the impairment of the conformational change of JRAB/MICAL-L2. This review begins with a brief history of image-based computational analyses on cell migration, explains why quantitative analysis of the stylization of collective cell behavior is difficult, and finally introduces our recent work on JRAB/MICAL-L2 as a successful example of the multidisciplinary approach combining cell biology, live imaging, and computational biology. In combination, these methods have enabled quantitative evaluations of the “dancing style” of collective cell migration

Detecting, segmenting and tracking bio-medical objects

Studying the behavior patterns of biomedical objects helps scientists understand the underlying mechanisms. With computer vision techniques, automated monitoring can be implemented for efficient and effective analysis in biomedical studies. Promising applications have been carried out in various research topics, including insect group monitoring, malignant cell detection and segmentation, human organ segmentation and nano-particle tracking. In general, applications of computer vision techniques in monitoring biomedical objects include the following stages: detection, segmentation and tracking. Challenges in each stage will potentially lead to unsatisfactory results of automated monitoring. These challenges include different foreground-background contrast, fast motion blur, clutter, object overlap and etc. In this thesis, we investigate the challenges in each stage, and we propose novel solutions with computer vision methods to overcome these challenges and help automatically monitor biomedical objects with high accuracy in different cases --Abstract, page iii

ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs