Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging

Authors:
Ilker Ersoy;Filiz Bunyak;Vadim Chagin;M. Christina Cardoso;Kannappan Palaniappan
Affiliations:
Department of Computer Science, University of Missouri Columbia, USA;Department of Computer Science, University of Missouri Columbia, USA;Department of Biology, Technische Universität Darmstadt, Germany and Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia;Department of Biology, Technische Universität Darmstadt, Germany;Department of Computer Science, University of Missouri Columbia, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

Citing 6
Cited 1

A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model

International Journal of Computer Vision
Graph Partitioning Active Contours (GPAC) for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy

Bioinformatics
A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Cell Spreading Analysis with Directed Edge Profile-Guided Level Set Active Contours

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Cell segmentation using coupled level sets and graph-vertex coloring

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Classification of cell cycle phases in 3D confocal microscopy using PCNA and chromocenter features

Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing

Quantified Score

Hi-index	0.00

Visualization

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.