  Intestinal enteroendocrine cells secrete hormones that are vital for the
regulation of glucose metabolism but their differentiation from intestinal stem
cells is not fully understood. Asymmetric stem cell divisions have been linked
to intestinal stem cell homeostasis and secretory fate commitment. We monitored
cell divisions using 4D live cell imaging of cultured intestinal crypts to
characterize division modes by means of measurable features such as orientation
or shape. A statistical analysis of these measurements requires annotation of
mitosis events, which is currently a tedious and time-consuming task that has
to be performed manually. To assist data processing, we developed a learning
based method to automatically detect mitosis events. The method contains a
dual-phase framework for joint detection of dividing cells (mothers) and their
progeny (daughters). In the first phase we detect mother and daughters
independently using Hough Forest whilst in the second phase we associate mother
and daughters by modelling their joint probability as Conditional Random Field
(CRF). The method has been evaluated on 32 movies and has achieved an AUC of
72%, which can be used in conjunction with manual correction and dramatically
speed up the processing pipeline.
