A benchmark for comparison of cell tracking algorithms

Abstract

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark.Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.Contact: codesolorzano@unav.esSupplementary information: Supplementary data are available at Bioinformatics online.

Quantifying trophoblast migration: In vitro approaches to address in vivo situations

“…34 The final option is by far the simplest way to quantify migration but it makes use of little of the rich data available from time-lapse imaging, and so provides the least mechanistic insight. Automatic cell detection methods are continually improving, 52,55 however at this time manual or semi-automatic methods with manual correction are still commonly used in practice and can provide the most accurate methods for analysis.…”

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

“…In this paper, our ambition is not to cover all the topics in bio-imaging but to present with more details a few important image processing problems and applications not addressed in the aforementioned challenges and surveys (e.g., [18], [21], [1], [3]). Accordingly, we focus arbitrarily on dedicated image processing and image analysis methods that generally are included into workflows developed for specific biological studies.…”

“…It has been later demonstrated that this motion is due to the thermal agitation in the medium resulting in shock between molecules and causing stochastic trajectories. By integration of the squared displacement along the trajectory over time, we get the fundamental Stokes-Einstein equation: (3) where in 2D and the scalar diffusion coefficient is linked to the viscosity of the medium and the size of the particle. Unrestricted free diffusion is then indicated by the linearity of the plot of MSD.…”

Segmentation and Shape Analysis of Macrophages Using Anglegram Analysis

“…Segmentation of cells in fluorescence microscopy is a widely studied area [5,6], with many approaches ranging from thresholding techniques [7], to active surfaces [8] and active contours [9]. In recent years, techniques such as adaptive active physical models [10] and multilevel sets [11] have been used to address the problem of cells that overlap in cervical cancer images.…”