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A biosegmentation benchmark for evaluation of bioimage analysis methods

Drelie Gelasca, Elisa; Obara, Boguslaw; Fedorov, Dmitry; Kvilekval, Kris; Manjunath, B.S.

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Elisa Drelie Gelasca

Boguslaw Obara

Dmitry Fedorov

Kris Kvilekval

B.S. Manjunath


Background: We present a biosegmentation benchmark that includes infrastructure, datasets with associated ground truth, and validation methods for biological image analysis. The primary motivation for creating this resource comes from the fact that it is very difficult, if not impossible, for an end-user to choose from a wide range of segmentation methods available in the literature for a particular bioimaging problem. No single algorithm is likely to be equally effective on diverse set of images and each method has its own strengths and limitations. We hope that our benchmark resource would be of considerable help to both the bioimaging researchers looking for novel image processing methods and image processing researchers exploring application of their methods to biology. Results: Our benchmark consists of different classes of images and ground truth data, ranging in scale from subcellular, cellular to tissue level, each of which pose their own set of challenges to image analysis. The associated ground truth data can be used to evaluate the effectiveness of different methods, to improve methods and to compare results. Standard evaluation methods and some analysis tools are integrated into a database framework that is available online at webcite. Conclusion: This online benchmark will facilitate integration and comparison of image analysis methods for bioimages. While the primary focus is on biological images, we believe that the dataset and infrastructure will be of interest to researchers and developers working with biological image analysis, image segmentation and object tracking in general.


Drelie Gelasca, E., Obara, B., Fedorov, D., Kvilekval, K., & Manjunath, B. (2009). A biosegmentation benchmark for evaluation of bioimage analysis methods. BMC Bioinformatics, 10, Article 368.

Journal Article Type Article
Acceptance Date Nov 1, 2009
Online Publication Date Nov 1, 2009
Publication Date Nov 1, 2009
Deposit Date Jul 31, 2012
Publicly Available Date Aug 26, 2015
Journal BMC Bioinformatics
Publisher BioMed Central
Peer Reviewed Peer Reviewed
Volume 10
Article Number 368


Published Journal Article (1.9 Mb)

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Copyright Statement
© 2009 Drelie Gelasca et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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