Open AccessPombeX: Robust Cell Segmentation for Fission Yeast Transillumination Images
Author(s) -
Jyh-Ying Peng,
Yen-Jen Chen,
Marc D. Green,
Sarah A. Sabatinos,
Susan L. Forsburg,
ChunNan Hsu
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0081434
Subject(s) - segmentation , spurious relationship , computer science , artificial intelligence , focus (optics) , vector flow , schizosaccharomyces pombe , pixel , image segmentation , computer vision , pattern recognition (psychology) , computational biology , saccharomyces cerevisiae , biology , yeast , genetics , physics , optics , machine learning
Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo . Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of a large amount of images, the first step of which requires robust segmentation of the cell. We developed a segmentation system, PombeX, that can segment cells from transmitted illumination images with focus gradient and varying contrast. Corrections for focus gradient are applied to the image to aid in accurate detection of cell membrane and cytoplasm pixels, which is used to generate initial contours for cells. Gradient vector flow snake evolution is used to obtain the final cell contours. Finally, a machine learning-based validation of cell contours removes most incorrect or spurious contours. Quantitative evaluations show overall good segmentation performance on a large set of images, regardless of differences in image quality, lighting condition, focus condition and phenotypic profile. Comparisons with recent related methods for yeast cells show that PombeX outperforms current methods, both in terms of segmentation accuracy and computational speed.