TY - GEN
T1 - Single cell tracking based on Voronoi partition via stable matching
AU - Chang, Young Hwan
AU - Linsley, Jeremy
AU - Lamstein, Josh
AU - Kalra, Jaslin
AU - Epstein, Irina
AU - Barch, Mariya
AU - Daily, Kenneth
AU - Synder, Phil
AU - Omberg, Larsson
AU - Heiser, Laura
AU - Finkbeiner, Steve
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as stable matching problem and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.
AB - Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as stable matching problem and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.
UR - http://www.scopus.com/inward/record.url?scp=85099885551&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099885551&partnerID=8YFLogxK
U2 - 10.1109/CDC42340.2020.9304436
DO - 10.1109/CDC42340.2020.9304436
M3 - Conference contribution
AN - SCOPUS:85099885551
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5086
EP - 5091
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Conference on Decision and Control, CDC 2020
Y2 - 14 December 2020 through 18 December 2020
ER -