Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms

Authors:
Luís Pedro Coelho;Aabid Shariff;Robert F. Murphy
Affiliations:
Lane Center for Computational Biology, Carnegie Mellon University and Center for Bioimage Informatics, Carnegie Mellon University and Joint Carnegie Mellon University, University of Pittsburgh;Lane Center for Computational Biology, Carnegie Mellon University and Center for Bioimage Informatics, Carnegie Mellon University and Joint Carnegie Mellon University, University of Pittsburgh;Lane Center for Comp. Biology, Carnegie Mellon Univ. and Center for Bioimage Informatics, Joint Carnegie Mellon Univ., Univ. of Pittsburgh and Dept. of Biological Sci., Biomedical Eng., and Freibu ...
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms. We focus on algorithms appropriate for high-throughput settings, where only minimal user intervention is feasible. The hand-labeled dataset (and all software used to compare methods) is publicly available to enable others to use it as a benchmark for newly proposed algorithms.