  Automating human preimplantation embryo grading offers the potential for
higher success rates with in vitro fertilization (IVF) by providing new
quantitative and objective measures of embryo quality. Current IVF procedures
typically use only qualitative manual grading, which is limited in the
identification of genetically abnormal embryos. The automatic quantitative
assessment of blastocyst expansion can potentially improve sustained pregnancy
rates and reduce health risks from abnormal pregnancies through a more accurate
identification of genetic abnormality. The expansion rate of a blastocyst is an
important morphological feature to determine the quality of a developing
embryo. In this work, a deep learning based human blastocyst image segmentation
method is presented, with the goal of facilitating the challenging task of
segmenting irregularly shaped blastocysts. The type of blastocysts evaluated
here has undergone laser ablation of the zona pellucida, which is required
prior to trophectoderm biopsy. This complicates the manual measurements of the
expanded blastocyst's size, which shows a correlation with genetic
abnormalities. The experimental results on the test set demonstrate
segmentation greatly improves the accuracy of expansion measurements, resulting
in up to 99.4% accuracy, 98.1% precision, 98.8% recall, a 98.4% Dice
Coefficient, and a 96.9% Jaccard Index.
