On-line Tracking of Cells and Their Lineage from Time Lapse Video Data
MetadataShow full item record
© 2018 IEEE. In this paper, we propose an algorithm for tracking cells that also provides lineage information. Our approach incorporates cell spawning into the random finite set dynamic model of the cell population, which allows the Bayes multi-object filter to capture information on the cells ancestries. A generalized Labeled Multi-Bernoulli (GLMB) filter (with cell spawning model) is applied to track the cells using detections extracted from time lapse video data. Numerical results on a set of stems cells demonstrate the capability of the proposed solution to track the time-varying number of cells as well as their ancestries.
Showing items related by title, author, creator and subject.
Schmeckpeper, J.; Ikeda, Y.; Kumar, A.; Metharom, Pat; Russell, S.; Caplice, N. (2009)Lentiviral vectors encoding for identifiable marker genes controlled by lineage-specific promoters can be used to track differentiation of bone marrow progenitors into endothelial cells and/or smooth muscle cells. Human ...
Kim, Du Yong; Vo, Ba-Ngu; Thian, A.; Choi, Y. (2017)© 2017 IEEE. Tracking is a means to accomplish the more fundamental task of extracting relevant information about cell behavior from time-lapse microscopy data. Hence, characterizing uncertainty or confidence in the ...
Photovoltaic cell modeling for maximum power point tracking using MATLAB/Simulink to improve the conversion efficiencyDas, Narottam; Wongsodihardjo, Hendy; Islam, Syed (2013)This paper focuses on the behavior of maximum power point tracking (MPPT) on photovoltaic (PV) cell systems using MATLAB/Simulink software. The PV cells can offer better simulation results when a double-exponential type, ...