Detecting Abnormal Cell Division Patterns in Early Stage Human Embryo Development

Recently, it has been shown that early division patterns, such as cell division timing biomarkers, are crucial to predict human embryo viability. Precise and accurate measurement of these markers requires cell lineage analysis to identify normal and abnormal division patterns. However, current approaches to early-stage embryo analysis only focus on estimating the number of cells and their locations, thus failing to detect abnormal division patterns and potentially yielding incorrect timing biomarkers. In this work we propose an automated tool that can perform lineage tree analysis up to the 5-cell stage, which is sufficient to accurately compute all the known important biomarkers. To this end, we introduce a CRF-based cell localization framework. We demonstrate the benefits of our approach on a data set of 22 human embryos, resulting in correct identification of all abnormal division patterns in the data set.