Temporal models for mitotic phase labelling.

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

Temporal models for mitotic phase labelling

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