TY - JOUR
T1 - Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis
AU - Li, Fuhai
AU - Zhou, Xiaobo
AU - Ma, Jinwen
AU - Wong, Stephen T.C.
N1 - Funding Information:
Manuscript received July 31, 2008; revised June 17, 2009; accepted July 02, 2009. First published July 28, 2009; current version published January 04, 2010. This work was supported by the National Institutes of Health under Grant NIH R01 LM008696. The work of S. T. C. Wong was supported by the Center for Bioinformatics Program Grant of Harvard Center of Neurodegeneration and Repair (now Harvard Neurodiscovery Center), Harvard Medical School. Asterisk indicates corresponding author.
PY - 2010/1
Y1 - 2010/1
N2 - Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cellcycle analysis software package, DCELLIQ, which is freely available.
AB - Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cellcycle analysis software package, DCELLIQ, which is freely available.
KW - Anti-cancer drug screening
KW - Cell cycle analysis
KW - Segmentation and tracking
KW - Time-lapse fluorescence microscopy
UR - http://www.scopus.com/inward/record.url?scp=73849143622&partnerID=8YFLogxK
U2 - 10.1109/TMI.2009.2027813
DO - 10.1109/TMI.2009.2027813
M3 - Article
C2 - 19643704
AN - SCOPUS:73849143622
SN - 0278-0062
VL - 29
SP - 96
EP - 105
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 1
M1 - 5175475
ER -