title: Automated 3-D tracking of centrosomes in sequences of confocal image stacks.
abstract: In order to facilitate the study of neuron migration, we propose a method for 3-D detection and tracking of centrosomes in time-lapse confocal image stacks of live neuron cells. We combine Laplacian-based blob detection, adaptive thresholding, and the extraction of scale and roundness features to find centrosome-like objects in each frame. We link these detections using the joint probabilistic data association filter (JPDAF) tracking algorithm with a Newtonian state-space model tailored to the motion characteristics of centrosomes in live neurons. We apply our algorithm to image sequences containing multiple cells, some of which had been treated with motion-inhibiting drugs. We provide qualitative results and quantitative comparisons to manual segmentation and tracking results showing that our average motion estimates agree to within 13% of those computed manually by neurobiologists.
authors: Image Science and Machine Vision Group, Measurement Science and Systems Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. kerekesra@ornl.gov
authors: Ryan A
authors: RA
authors: Kerekes
authors: Shaun S
authors: SS
authors: Gleason
authors: Niraj
authors: N
authors: Trivedi
authors: David J
authors: DJ
authors: Solecki
mesh_terms: D000465:Algorithms; D000818:Animals; D001698:Biomedical Engineering; D002465:Cell Movement; D018385:Centrosome / Q000502:physiology / Q000648:ultrastructure*; D049452:Green Fluorescent Proteins / Q000378:metabolism; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000706:statistics & numerical data; D066298:In Vitro Techniques; D018613:Microscopy, Confocal / Q000379:methods* / Q000706:statistics & numerical data; D009068:Movement; D009474:Neurons / Q000502:physiology / Q000648:ultrastructure; D011994:Recombinant Proteins / Q000378:metabolism
chemical_list: D011994:Recombinant Proteins; D049452:Green Fluorescent Proteins