TY - GEN
T1 - Optimal live cell tracking for cell cycle study using time-lapse fluorescent microscopy images
AU - Li, Fuhai
AU - Zhou, Xiaobo
AU - Wong, Stephen T.C.
PY - 2010
Y1 - 2010
N2 - Cell cycle study using time-lapse fluorescent microscopy images is important for understanding the mechanisms of cell division and screening of anti-cancer drugs. Cell tracking is necessary for quantifying cell behaviors. However, the complex behaviors and similarity of individual cells in a dense population make the cell population tracking challenging. To deal with these challenges, we propose a novel tracking algorithm, in which the local neighboring information is introduced to distinguish the nearby cells with similar morphology, and the Interacting Multiple Model (IMM) filter is employed to compensate for cell migrations. Based on a similarity metric, integrating the local neighboring information, migration prediction, shape and intensity, the integer programming is used to achieve the most stable association between cells in two consecutive frames. We evaluated the proposed method on the high content screening assays of HeLa cancer cell populations, and achieved 92% average tracking accuracy.
AB - Cell cycle study using time-lapse fluorescent microscopy images is important for understanding the mechanisms of cell division and screening of anti-cancer drugs. Cell tracking is necessary for quantifying cell behaviors. However, the complex behaviors and similarity of individual cells in a dense population make the cell population tracking challenging. To deal with these challenges, we propose a novel tracking algorithm, in which the local neighboring information is introduced to distinguish the nearby cells with similar morphology, and the Interacting Multiple Model (IMM) filter is employed to compensate for cell migrations. Based on a similarity metric, integrating the local neighboring information, migration prediction, shape and intensity, the integer programming is used to achieve the most stable association between cells in two consecutive frames. We evaluated the proposed method on the high content screening assays of HeLa cancer cell populations, and achieved 92% average tracking accuracy.
KW - Cell cycle progression
KW - Cell tracking
KW - Drug screening
KW - Interacting Multiple Model
KW - Voronoi Tessellation
UR - http://www.scopus.com/inward/record.url?scp=77958045939&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15948-0_16
DO - 10.1007/978-3-642-15948-0_16
M3 - Conference contribution
AN - SCOPUS:77958045939
SN - 3642159478
SN - 9783642159473
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 124
EP - 131
BT - Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings
T2 - 1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010
Y2 - 20 September 2010 through 20 September 2010
ER -