BLAST-NET: Semantic Segmentation of Human Blastocyst Components via Cascaded Atrous Pyramid and Dense Progressive Upsampling

Abstract

Components of a human blastocyst (day-5 embryo) and their morphological attributes highly correlate with the embryo's potentials for a viable pregnancy. Automatic semantic segmentation of human blastocyst components is a crucial step toward achieving objective quality assessment of such blastocyst. In this paper, a semantic segmentation system is proposed for human blastocyst components in microscopic images. The proposed Blast-Net features two novel components: a Cascaded Atrous Pyramid Pooling (CAPP) module to incorporate multi-scale global contextual priors, and a Dense Progressive Sub-pixel Upsampling (DPSU) module to recover the high-resolution prediction map. Experimental results confirm that the proposed method achieves the best-reported segmentation performance to date with a mean Jaccard Index of 82.85 % for microscopic images of the human blastocyst.