Automatic Segmentation of Trophectoderm in Microscopic Images of Human Blastocysts

Abstract

Accurate assessment of embryos viability is an extremely important task in the optimization of in vitro fertilization treatment outcome. One of the common ways of assessing the quality of a human embryo is grading it on its fifth day of development based on morphological quality of its three main components (Trophectoderm, Inner Cell Mass, and the level of expansion or the thickness of its Zona Pellucida). In this study, we propose a fully automatic method for segmentation and measurement of TE region of blastocysts (day-5 human embryos). Here, we eliminate the inhomogeneities of the blastocysts surface using the Retinex theory and further apply a level-set algorithm to segment the TE regions. We have tested our method on a dataset of 85 images and have been able to achieve a segmentation accuracy of 84.6% for grade A, 89.0% for grade B, and 91.7% for grade C embryos.

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