title: Algorithms for the automated analysis of cellular dynamics within living fungal colonies.
abstract: We present robust and efficient algorithms to automate the measurement of nuclear movement and germ tube extension rates in living fungal networks. The aim is to facilitate the understanding of the dynamics and regulation of nuclear migration in growing fungal colonies. The proposed methodology combines a cascade correlation filter to identify nuclear centers from which 2D nuclear velocities are determined and a level set algorithm for centerline extraction to monitor spore (conidial) germling growth. We show how the proposed cascaded filter improves spatial resolution in the presence of noise and is robust when fluorescently labeled nuclei with different intensities are in close proximity to each other. The performance of the filter is evaluated by simulation in comparison to the well known Rayleigh and Sparrow criteria, and experimental evidence is given from clusters of nuclei and nuclei undergoing mitotic division. The capabilities developed have enabled the robust and objective analysis of 10's of Gigabytes of image data that is being exploited by biological scientists.
authors.affiliation: School of Mechanical Engineering, University of Leeds, Leeds LS29JT, United Kingdom. menncaj@leeds.ac.uk
authors.forename: N C
authors.initials: NC
authors.lastname: Angarita-Jaimes
authors.forename: M G M
authors.initials: MG
authors.lastname: Roca
authors.forename: C E
authors.initials: CE
authors.lastname: Towers
authors.forename: N D
authors.initials: ND
authors.lastname: Read
authors.forename: D P
authors.initials: DP
authors.lastname: Towers
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002467:Cell Nucleus / Q000502:physiology; D018613:Microscopy, Confocal; D009492:Neurospora crassa / Q000254:growth & development*; D010363:Pattern Recognition, Automated