Automatic blastomere detection in day 1 to day 2 human embryo images using partitioned graphs and ellipsoids

Fertility specialists have linked the size, shape and position of blastomeres in humans embryos with the viability of such embryos. We propose an automatic blastomere identification and modeling approach in an attempt to aid physicians in determining embryo's viability. The proposed method applies isoperimetric graph partitioning, succeeded by a novel region merging algorithm to Hoffman Modulation Contrast (HMC) embryo images, to approximate blastomeres positions. Ellipsoidal models are then used to approximate the shape and the size of each blastomere. We discuss experimental results on a dataset of 40 embryo images, and expand on the advantages and drawbacks of our method while comparing our method to other approaches.