An automated blastomere identification method for the evaluation of day 2 embryos during IVF/ICSI treatments. (March 2018) - Strouthopoulos, Charalambos; Anifandis, George
Highlights: Evaluation of embryo morphology during IVF is an important issue. Up to date embryo evaluation is performed by embryologists by estimating morphology in terms of the number of blastomeres, percent of fragmentation a simple microscope or using time lapse monitoring. In this work a new bioinformatics approach for the evaluation of embryos has been applied. This method uses the number of blastomeres and the mean eccentricity of each blastomere. This new approach seems to be accurate for IVF human day 2 embryos helping also statistically the evaluation of those embryos. Abstract: Purpose: Evaluation of human embryos is one of the most important challenges in vitro fertilization (IVF) programs. The morphology and the morphokinetic parameters of the early cleaving embryo are of critical clinical importance. This stage spans the first 48 h post-fertilization, in which the embryo is dividing in smaller blastomeres at specific time-points. The morphology, in combination with the symmetry of the blastomeres seems to be powerful features with strong prognostic value for embryo evaluation. To date, the identification of these features is based on human inspection in timed intervals, at best using camera systems that simply work as surveillance systems without any precise alerting and decision support mechanisms. The purpose of the study presented in this paper was to develop a computer vision technique to automatically detect and identify the most suitable cleaving embryos (preferably at day 2 post-fertilization) for embryo transfer (ET) during IVF/ICSI treatments. Methods and results: To this end, texture and geometrical features were used to localize and analyze the whole cleaving embryo in 2D grayscale images captured during in vitro embryo formation. Because of the ellipsoidal nature of blastomeres, the contour of each blastomere was modeled with an optimal fitting ellipse while the mean eccentricity of all ellipses is computed. The mean eccentricity in combination with the number of blastomeres forms the feature space on which the final criterion for the embryo evaluation was based. Conclusions: Experimental results with low quality 2D grayscale images demonstrated the effectiveness of the proposed technique and provided evidence of a novel automated approach for predicting embryo quality.