Bayesian Multi-Object Tracking for Cell Microscopy
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Cell tracking is an essential tool for studying how cells behave and divide under different conditions. This thesis proposes new approaches to track cells and their lineages using random finite set, which allows the tracking errors to be statistically quantified. Additionally, this thesis also explores criteria to rank performance of basic vision task algorithms (e.g., object detection, instance-level segmentation, and tracking), which have not been received proportionate attention from the scientific community.
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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 ...
Dat Nguyen, T.; Kim, Du Yong (2018)© 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 ...
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 ...