title: PombeX: robust cell segmentation for fission yeast transillumination images.
abstract: 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.
authors.affiliation: Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan, R.O.C. ; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan, R.O.C. ; Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan, R.O.C.
authors.forename: Jyh-Ying
authors.initials: JY
authors.lastname: Peng
authors.affiliation: Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan, R.O.C.
authors.forename: Yen-Jen
authors.initials: YJ
authors.lastname: Chen
authors.affiliation: Molecular and Computational Biology Program, University of Southern California, Los Angeles, United States of America.
authors.forename: Marc D
authors.initials: MD
authors.lastname: Green
authors.affiliation: Molecular and Computational Biology Program, University of Southern California, Los Angeles, United States of America.
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Sabatinos
authors.affiliation: Molecular and Computational Biology Program, University of Southern California, Los Angeles, United States of America.
authors.forename: Susan L
authors.initials: SL
authors.lastname: Forsburg
authors.affiliation: Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, La Jolla, United States of America ; Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C. ; Information Sciences Institute, University of Southern California, Marina del Rey, California, United States of America.
authors.forename: Chun-Nan
authors.initials: CN
authors.lastname: Hsu
mesh_terms: D001185:Artificial Intelligence; D002451:Cell Compartmentation; D005453:Fluorescence; D005656:Fungal Proteins / Q000378:metabolism*; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012568:Schizosaccharomyces / Q000166:cytology*; D012984:Software
chemical_list: D005656:Fungal Proteins