A Hierarchical Bayesian Approach for Unsupervised Cell Phenotype Clustering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)


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.


Hierarchical Bayesian methods Hidden Markov Models Cell phenotypes Unsupervised clustering Mitosis phase modeling Time-lapse microscopy 



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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Vision GroupFriedrich Schiller University JenaJenaGermany

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