title: Temporal models for mitotic phase labelling.
abstract: With the widespread use of time-lapse data to understand cellular function, there is a need for tools which facilitate high-throughput analysis of data. Fluorescence microscopy of genetically engineered cell lines in culture can be used to visualise the progression of these cells through the cell cycle, including distinctly identifiable sequential stages of cell division (mitotic phases). We present a system for automated segmentation and mitotic phase labelling using temporal models. This work takes the novel approach of using temporal features evaluated over the whole of the mitotic phases rather than over single frames, thereby capturing the distinctive behaviour over the phases. We compare and contrast three different temporal models: Dynamic Time Warping, Hidden Markov Models, and Semi Markov Models. A new loss function is proposed for the Semi Markov model to make it more robust to inconsistencies in data annotation near transition boundaries. The models are tested under two different experimental conditions to explore robustness to changes in biological conditions.
authors: Department of Engineering Science, University of Oxford, United Kingdom. Electronic address: amr@robots.ox.ac.uk.
authors: A
authors: A
authors: El-Labban
authors: Department of Engineering Science, University of Oxford, United Kingdom.
authors: A
authors: A
authors: Zisserman
authors: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
authors: Y
authors: Y
authors: Toyoda
authors: Max Planck Institute for Molecular Physiology, Dortmund, Germany.
authors: A W
authors: AW
authors: Bird
authors: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
authors: A
authors: A
authors: Hyman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D058948:Cell Tracking / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D008390:Markov Chains; D008856:Microscopy, Fluorescence; D008938:Mitosis / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059008:Time-Lapse Imaging / Q000379:methods*
keywords: Dynamic time warping; Fluorescence microscopy; Hidden Markov model; Semi Markov model; Temporal model