title: Segmentation and classification of cell cycle phases in fluorescence imaging.
abstract: Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.
authors.affiliation: Department of Computer Science, University of Missouri Columbia, USA.
authors.forename: Ilker
authors.initials: I
authors.lastname: Ersoy
authors.forename: Filiz
authors.initials: F
authors.lastname: Bunyak
authors.forename: Vadim
authors.initials: V
authors.lastname: Chagin
authors.forename: M Christina
authors.initials: MC
authors.lastname: Cardoso
authors.forename: Kannappan
authors.initials: K
authors.lastname: Palaniappan
mesh_terms: D000465:Algorithms; D002453:Cell Cycle; D002467:Cell Nucleus / Q000648:ultrastructure*; D006367:HeLa Cells; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity