Image Processing and Deep Learning Methods for the Semantic Segmentation of Blastocyst Structures

Embryo selection is an indispensable step to ensure the success of in vitro fertilization. There are two techniques to perform embryo selection: preimplantation genetic screening and embryo morphological grading. However, even with these techniques, the embryo implantation probability is barely 65% making extremely difficult to evaluate their implantation potential. This is mainly due to the lack of markers, and the subjectivity associated with experience, judgment, and training of the embryologists. Computer vision and deep learning methods can help to automatically identify those markers with methods such as the segmentation of the embryo structures to offer detailed, quantitative, and objective assessments; and with that, information to predict the pregnancy outcome of embryos. In this paper, we present different methods capable of segmenting the components of an embryo (namely, the Trophectoderm, the Inner Cell Mass and the Zona Pellucida) with Dice scores ranging from 0.85 to 0.89, and openly release the code so that anyone can use it and replicate the results. These models are a first step towards a more objective evaluation of the embryos' implantation potential.