Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging
By Ilker Ersoy, Filiz Bunyak, V. Chagin, M. Christina Cardoso, Kannappan K. Palaniappan. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2009
338 citations recorded by the source corpus.
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.
Subfields: Biophysics, Media Technology
View Paper: https://www.semanticscholar.org/paper/ca02283c1709416b4936ba33f49f06a2a507e745
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Citations in the corpus
- Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “Another family of generative models for label fusion can be viewed as a modification of Sabuncu’s model (Sabuncu et al., 2009, 2010), where the latent membership field M is only used to define a prior on the labels and the novel image intensities are generated directly from the underlying…”
- Comparison of parameter‐adapted segmentation methods for fluorescence micrographs (2011, neutral): “…extended-energy function for segmentation of mono-verlapping cells stemming from the human hepatoma cell line (HUH7 cells) (26), while Ersoy et al. (27) used level set-based multiple-phase fast graph partitioning active contours (Fas-tGPAC), a method that is an extension of graph partitioning…”