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

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3 Author(s)
Coelho, L.P. ; Lane Center for Comput. Biol., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Shariff, A. ; Murphy, R.F.

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.

Published in:

Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on

Date of Conference:

June 28 2009-July 1 2009

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