Discriminative Semi-Markov Models for automated 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. We present a system for automated segmentation and mitotic phase labelling based on a wide margin discriminative Semi-Markov Model. 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. This approach extends and substantially improves on our previous approach of using dynamic time warping to align temporal feature signals to a reference.