Benchmark for multi-cellular segmentation of bright field microscopy images

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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
Volume14
DOIs
StatePublished - 7 Nov 2013
Externally publishedYes

Keywords

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

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