Mitosis detection for stem cell tracking in phase-contrast microscopy images

Automated visual-tracking systems of stem cell populations in vitro allow for high-throughput analysis of time-lapse phase-contrast microscopy. In these systems, detection of mitosis, or cell division, is critical to tracking performance as mitosis causes branching of the trajectory of a mother cell into the two trajectories of its daughter cells. Recently, one mitosis detection algorithm showed its success in detecting the time and location that two daughter cells first clearly appear as a result of mitosis. This detection result can therefore helps trajectories to correctly bifurcate and the relations between mother and daughter cells to be revealed. In this paper, we demonstrate that the functionality of this recent mitosis detection algorithm significantly improves state-of-the-art cell tracking systems through extensive experiments on 48 C2C12 myoblastic stem cell populations under four different conditions.

[1]  Milan Sonka,et al.  Cell Segmentation, Tracking, and Mitosis Detection Using Temporal Context , 2005, MICCAI.

[2]  Takeo Kanade,et al.  Automated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy Images , 2011, IEEE Transactions on Medical Imaging.

[3]  D C Barber,et al.  Automated tracking of migrating cells in phase‐contrast video microscopy sequences using image registration , 2009, Journal of microscopy.

[4]  Takeo Kanade,et al.  Computer vision tracking of stemness , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[5]  Takeo Kanade,et al.  Cell population tracking and lineage construction with spatiotemporal context , 2008, Medical Image Anal..

[6]  Takeo Kanade,et al.  Cell image analysis: Algorithms, system and applications , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[7]  Takeo Kanade,et al.  Understanding the Optics to Aid Microscopy Image Segmentation , 2010, MICCAI.

[8]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[9]  Jens Rittscher,et al.  Spatio-temporal cell cycle phase analysis using level sets and fast marching methods , 2009, Medical Image Anal..

[10]  Takeo Kanade,et al.  Mitosis sequence detection using hidden conditional random fields , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Philippe Van Ham,et al.  Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes , 2005, IEEE Transactions on Medical Imaging.

[12]  B. Roysam,et al.  Automated Cell Lineage Construction: A Rapid Method to Analyze Clonal Development Established with Murine Neural Progenitor Cells , 2006, Cell cycle.

[13]  Margrit Betke,et al.  Tracking of cell populations to understand their spatio-temporal behavior in response to physical stimuli , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[14]  R. Kiss,et al.  Videomicroscopic extraction of specific information on cell proliferation and migration in vitro. , 2008, Experimental cell research.