Blastomere Cell Counting and Centroid Localization in Microscopic Images of Human Embryo

Abstract

The time of the first cell cleavage in the embryonic development of a human embryo is an important indicator of the embryo's potential for developing into a healthy baby. The time and synchronicity of following cleavages are also linked to the quality of an embryo. In this paper, a deep learning based framework is proposed to take on the challenging task of automatic counting and centroid localization of embryonic cells (blastomeres) in microscopic images of human embryos. In particular, ensemble of residual dilated UNet is proposed to count blastomeres and localize their centroids. Experimental results confirm that the proposed framework is capable of counting blastomeres in a densely occupied and overlapping space of human embryo by an average accuracy of 88.2% for embryos of 1 - 5 cells.