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We address the problem of counting cells in time-lapse microscopy images of developing human embryos. Cell counting is considered as an important step in analyzing biological phenomenon such as embryo viability. Traditional approaches to counting cells rely on hand crafted features and cannot fully take advantage of the growth in data set sizes. In this paper, we propose a framework to automatically count the number of cells in developing human embryos. The framework employs a deep convolutional neural network model trained to count cells from raw microscopy images. We demonstrate the effectiveness of our approach on a data set of 265 human embryos. The results show that the proposed framework provides robust estimates of the number of cells in a developing embryo up to the 5-cell stage (i.e., 48 h post fertilization).

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.