DocumentCode :
2920635
Title :
Detection of mitosis within a stem cell population of high cell confluence in phase-contrast microscopy images
Author :
Huh, Seungil ; Chen, Mei
Author_Institution :
Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2011
fDate :
20-25 June 2011
Firstpage :
1033
Lastpage :
1040
Abstract :
Computer vision analysis of cells in phase-contrast microscopy images enables long-term continuous monitoring of live cells, which has not been feasible using the existing cellular staining methods due to the use of fluorescence reagents or fixatives. In cell culture analysis, accurate detection of mitosis, or cell division, is critical for quantitative study of cell proliferation. In this work, we present an approach that can detect mitosis within a cell population of high cell confluence, or high cell density, which has proven challenging because of the difficulty in separating individual cells. We first detect the candidates for birth events that are defined as the time and location at which mitosis is complete and two daughter cells first appear. Each candidate is then examined whether it is real or not after incorporating spatio-temporal information by tracking the candidate in the neighboring frames. For the examination, we design a probabilistic model named Two-Labeled Hidden Conditional Random Field (TL-HCRF) that can use the information on the timing of the candidate birth event in addition to the visual change of cells over time. Applied to two cell populations of high cell confluence, our method considerably outperforms previous methods. Comparisons with related statistical models also show the superiority of TL-HCRF on the proposed task.
Keywords :
biology computing; botany; cellular biophysics; computer vision; computerised monitoring; probability; TL-HCRF; candidate birth event; cell proliferation; computer vision analysis; culture analysis; daughter cells; fluorescence reagent; high cell confluence; live cell division; long-term continuous monitoring; mitosis detection; neighboring frame; phase contrast microscopy image; probabilistic model; spatio-temporal information; statistical model; stem cell population; two-labeled hidden conditional random field; Event detection; Microscopy; Support vector machines; Timing; Tracking; Training; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
ISSN :
1063-6919
Print_ISBN :
978-1-4577-0394-2
Type :
conf
DOI :
10.1109/CVPR.2011.5995717
Filename :
5995717
Link To Document :
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