Abstract
New technology for automated biological image acquisition has introduced the need for effective biological image analysis methods. These algorithms are constantly being developed by pattern recognition and machine vision experts, who tailor general computer vision techniques to the specific needs of biological imaging. However, computer scientists do not always have access to biological image datasets that can be used for computer vision research, and biologist collaborators who can assist in defining the biological questions are not always available. Here, we propose a publicly available benchmark suite of biological image datasets that can be used by machine vision experts for developing and evaluating biological image analysis methods. The suite represents a set of practical real-life imaging problems in biology, and offers examples of organelles, cells and tissues, imaged at different magnifications and different contrast techniques. All datasets are available for free download at http://ome.grc.nia.nih.gov/iicbu2008.
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Acknowledgments
This research was supported by the Intramural Research Program of the NIH, National Institute on Aging. The datasets Hela and CHO are from Murphy lab, CMU. The dataset Pollen was contributed by Andrew Duller, Henry Lamb and Ian France, and the dataset Binucleate was provided by Aaron Straight, Stanford U. We would also like to thank Cathy Wolkow and Wendy Iser for their assistance with the acquisition and definition of the Terminal Bulb Aging and C. elegans Muscle Aging datasets, and Elaine Jaffe for providing the data for the Lymphoma dataset.
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Shamir, L., Orlov, N., Mark Eckley, D. et al. IICBU 2008: a proposed benchmark suite for biological image analysis. Med Biol Eng Comput 46, 943–947 (2008). https://doi.org/10.1007/s11517-008-0380-5
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DOI: https://doi.org/10.1007/s11517-008-0380-5