A Linear Chain Markov Model for Detection and Localization of Cells in Early Stage Embryo Development

We address the problem of detecting and localizing cells in time lapse microscopy images during early stage embryo development. Our approach is based on a linear chain Markov model that estimates the number and location of cells at each time step. The state space for each time step is derived from a randomized ellipse fitting algorithm that attempts to find individual cell candidates within the embryo. These cell candidates are combined into embryo hypotheses, and our algorithm finds the most likely sequence of hypotheses over all time steps. We restrict our attention to detect and localize up to four cells, which is sufficient for many important applications such as predicting blastocyst and can be used for assessing embryos in in vitro fertilization procedures. We evaluate our method on twelve sequences of developing embryos and find that we can reliably detect and localize cells up to the four cell stage.