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Benchmark for multi-cellular segmentation of bright field microscopy images

Assaf Zaritsky1*, Nathan Manor1, Lior Wolf1, Eshel Ben-Jacob234 and Ilan Tsarfaty5*

Author Affiliations

1 Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel

2 School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel-Aviv, 69978, Israel

3 Center for Theoretical Biological Physics, Rice University, Houston, TX, 77005-1827, USA

4 Research & Development Unit Assaf Harofeh Medical Center, Zerifin, 70300, Israel

5 Department of Clinical Microbiology and Immunology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel

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BMC Bioinformatics 2013, 14:319  doi:10.1186/1471-2105-14-319

Published: 7 November 2013



Multi-cellular segmentation of bright field microscopy images is an essential computational step when quantifying collective migration of cells in vitro. Despite the availability of various tools and algorithms, no publicly available benchmark has been proposed for evaluation and comparison between the different alternatives.


A uniform framework is presented to benchmark algorithms for multi-cellular segmentation in bright field microscopy images. A freely available set of 171 manually segmented images from diverse origins was partitioned into 8 datasets and evaluated on three leading designated tools.


The presented benchmark resource for evaluating segmentation algorithms of bright field images is the first public annotated dataset for this purpose. This annotated dataset of diverse examples allows fair evaluations and comparisons of future segmentation methods. Scientists are encouraged to assess new algorithms on this benchmark, and to contribute additional annotated datasets.

Collective cell migration; Wound healing assay; Segmentation; Benchmarking