Benchmark for multi-cellular segmentation of bright field microscopy images

Assaf Zaritsky*, Nathan Manor, Lior Wolf, Eshel Ben-Jacob, Ilan Tsarfaty

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Background: 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.Description: 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.Conclusions: 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.

Original languageEnglish
Article number319
JournalBMC Bioinformatics
StatePublished - 7 Nov 2013


FundersFunder number
Tauber Family Foundation
National Science FoundationPHY-0822283
Directorate for Mathematical and Physical Sciences0822283
Breast Cancer Research Foundation
Cancer Prevention and Research Institute of Texas
Rice University
United States-Israel Binational Science Foundation
Tel Aviv University


    • Benchmarking
    • Collective cell migration
    • Segmentation
    • Wound healing assay


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