Article

Methods for cell and particle tracking

Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.
Methods in enzymology (Impact Factor: 2.19). 01/2012; 504:183-200. DOI: 10.1016/B978-0-12-391857-4.00009-4
Source: PubMed

ABSTRACT Achieving complete understanding of any living thing inevitably requires thorough analysis of both its anatomic and dynamic properties. Live-cell imaging experiments carried out to this end often produce massive amounts of time-lapse image data containing far more information than can be digested by a human observer. Computerized image analysis offers the potential to take full advantage of available data in an efficient and reproducible manner. A recurring task in many experiments is the tracking of large numbers of cells or particles and the analysis of their (morpho)dynamic behavior. In the past decade, many methods have been developed for this purpose, and software tools based on these are increasingly becoming available. Here, we survey the latest developments in this area and discuss the various computational approaches, software tools, and quantitative measures for tracking and motion analysis of cells and particles in time-lapse microscopy images.

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    • "Inferring dynamic quantities from this static data is an important task that has many applications in biology and related fields. The field of cell tracking arose from this need and is concerned with the development of methods to track and analyse dynamic cell shape changes from a series of still images captured within a time frame (see for example [2] [3] for reviews). On the other hand, a major focus of current research is the derivation of mathematical models for cell migration based on physical principles, e.g., [4]. "
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    ABSTRACT: Cell tracking algorithms which automate and systematise the analysis of time lapse image data sets of cells are an indispensable tool in the modelling and understanding of cellular phenomena. In this study we present a theoretical framework and an algorithm for whole cell tracking. Within this work we consider that "tracking" is equivalent to a dynamic reconstruction of the whole cell data (morphologies) from static image datasets. The novelty of our work is that the tracking algorithm is driven by a model for the motion of the cell. This model may be regarded as a simplification of a recently developed physically meaningful model for cell motility. The resulting problem is the optimal control of a geometric evolution law and we discuss the formulation and numerical approximation of the optimal control problem. The overall goal of this work is to design a framework for cell tracking within which the recovered data reflects the physics of the forward model. A number of numerical simulations are presented that illustrate the applicability of our approach.
    Journal of Computational Physics 04/2015; 297. DOI:10.1016/j.jcp.2015.05.014 · 2.49 Impact Factor
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    • "Digitization and Analysis.—We determined seed position in the videos by manually digitizing two points (distal wing tip, seed body center) using the MTrackJ plugin (Meijering and Smal 2012) for ImageJ (NIH, Bethesda, Md.). All videos in which we observed autorotation were digitized, for a total of 60,176 points. "
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    Paleobiology 03/2015; 41(2):205-225. DOI:10.1017/pab.2014.18 · 2.46 Impact Factor
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    • "Current tracking approaches can be divided into Tracking by Model Evolution and Tracking by Detection [26]. We briefly discuss state-of-the-art representatives of these two classes below and refer the interested reader to the much more complete recent surveys [27] [26] "
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    ABSTRACT: We propose a novel approach to automatically detecting and tracking cell populations in time-lapse images. Unlike earlier ones that rely on linking a predetermined and potentially under-complete set of detections, we generate an overcomplete set of competing detection hypotheses. We then perform detection and tracking simultaneously by solving an integer program to find an optimal and consistent subset. 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 image sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.
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