Abstract
We propose a hierarchical Bayesian model - the wordless Hierarchical Dirichlet Processes-Hidden Markov Model (wHDP-HMM), to tackle the problem of unsupervised cell phenotype clustering during the mitosis stages. Our model combines the unsupervised clustering capabilities of the HDP model with the temporal modeling aspect of the HMM. Furthermore, to model cell phenotypes effectively, our model uses a variant of the HDP, giving preference to morphology over co-occurrence. This is then used to model individual cell phenotype time series and cluster them according to the stage of mitosis they are in. We evaluate our method using two publicly available time-lapse microscopy video data-sets and demonstrate that the performance of our approach is generally better than the state-of-the-art.
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The authors gratefully acknowledge financial support by ZEISS and would like to thank Christian Wojek and Stefan Saur (ZEISS Corporate Research and Technology) for helpful discussions and suggestions.
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Krishna, M.V., Denzler, J. (2014). A Hierarchical Bayesian Approach for Unsupervised Cell Phenotype Clustering. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_6
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DOI: https://doi.org/10.1007/978-3-319-11752-2_6
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