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Träfflista för sökning "WFRF:(Coraluppi Stefano) "

Search: WFRF:(Coraluppi Stefano)

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1.
  • Chenouard, Nicolas, et al. (author)
  • Objective comparison of particle tracking methods
  • 2014
  • In: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; 11:3, s. 281-U247
  • Journal article (peer-reviewed)abstract
    • Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
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2.
  • Maska, Martin, et al. (author)
  • A benchmark for comparison of cell tracking algorithms
  • 2014
  • In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 30:11, s. 1609-1617
  • Journal article (peer-reviewed)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.
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