TY - JOUR
T1 - Automated quantification of cellular traffic in living cells
AU - Broeke, J.H.P.
AU - Ge, H.F.
AU - Dijkstra, I.M.
AU - Cemgil, A.T.
AU - Riedl, J.A.
AU - Cornelisse, L.N.
AU - Toonen, R.F.
AU - Verhage, M.
AU - Fitzgerald, W.J.
PY - 2009
Y1 - 2009
N2 - Cellular traffic is a central aspect of cell function in health and disease. It is highly dynamic, and can be investigated at increasingly finer temporal and spatial resolution due to new imaging techniques and probes. Manual tracking of these data is labor-intensive and observer-biased and existing automation is only semi-automatic and requires near-perfect object detection and high-contrast images. Here, we describe a novel automated technique for quantifying cellular traffic. Using local intrinsic information from adjacent images in a sequence and a model for object characteristics, our approach detects and tracks multiple objects in living cells via Multiple Hypothesis Tracking and handles several confounds (merge/split, birth/death, and clutters), as reliable as expert observers. By replacing the related component (e.g. using a different appearance model) the method can be easily adapted for quantitative analysis of other biological samples. © 2008 Elsevier B.V. All rights reserved.
AB - Cellular traffic is a central aspect of cell function in health and disease. It is highly dynamic, and can be investigated at increasingly finer temporal and spatial resolution due to new imaging techniques and probes. Manual tracking of these data is labor-intensive and observer-biased and existing automation is only semi-automatic and requires near-perfect object detection and high-contrast images. Here, we describe a novel automated technique for quantifying cellular traffic. Using local intrinsic information from adjacent images in a sequence and a model for object characteristics, our approach detects and tracks multiple objects in living cells via Multiple Hypothesis Tracking and handles several confounds (merge/split, birth/death, and clutters), as reliable as expert observers. By replacing the related component (e.g. using a different appearance model) the method can be easily adapted for quantitative analysis of other biological samples. © 2008 Elsevier B.V. All rights reserved.
U2 - https://doi.org/10.1016/j.jneumeth.2008.12.018
DO - https://doi.org/10.1016/j.jneumeth.2008.12.018
M3 - Article
C2 - 19146878
SN - 0165-0270
VL - 178
SP - 378
EP - 384
JO - Journal of neuroscience methods
JF - Journal of neuroscience methods
IS - 2
ER -