Multi-Resolutional Ensemble of Stacked Dilated U-Net for Inner Cell Mass Segmentation in Human Embryonic Images

Abstract

Identifying different components of a developing human embryo is a crucial step toward achieving automatic objective quality assessment of such embryo. Inner Cell Mass (ICM), part of the embryo that will eventually develop into a fetus, is one of the most important components of a human blastocyst and its morphological attributes are highly correlated with the overall quality of the embryo. In this paper, a deep learning based semantic segmentation approach is proposed to take on the challenging task of ICM segmentation. Particularly, multi-resolutional ensemble of stacked dilated U-Net is proposed for accurate segmentation of this region. Experimental results confirm that the proposed method achieves the best reported results to date with average Precision of 88.6%, Recall of 91.5%, Accuracy of 98.3%, Dice Coefficient of 89.5% and Jaccard Index of 81.6%.