Technological Educational Institute of Central Macedonia, Department of Informatics Engineering, Serres, Greece.
University of Thessaly, Department of Obstetrics and Gynecology, Assisted Reproduction Unit, Laboratory of Embryology, School of Health Sciences, Faculty of Medicine, Larisa, Greece.
Comput Methods Programs Biomed. 2018 Mar;156:53-59. doi: 10.1016/j.cmpb.2017.12.022. Epub 2017 Dec 22.
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.
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.
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.
胚胎评估是体外受精（IVF）项目中最重要的挑战之一。早期卵裂胚胎的形态和形态动力学参数具有重要的临床意义。这一阶段跨越受精后 48 小时，胚胎在特定时间点以更小的卵裂球分裂。形态学，结合卵裂球的对称性，似乎是具有强大预测价值的胚胎评估的有力特征。迄今为止，这些特征的识别是基于在定时间隔内进行的人工检查，最好使用仅作为监控系统的相机系统，而没有任何精确的警报和决策支持机制。本文介绍的研究旨在开发一种计算机视觉技术，以自动检测和识别最适合进行胚胎转移（ET）的卵裂胚胎（最好在受精后第 2 天），用于 IVF/ICSI 治疗。
为此，使用纹理和几何特征来定位和分析在体外胚胎形成过程中捕获的 2D 灰度图像中的整个卵裂胚胎。由于卵裂球的椭圆形性质，每个卵裂球的轮廓都用最佳拟合的椭圆进行建模，同时计算所有椭圆的平均偏心率。平均偏心率与卵裂球的数量相结合，形成了评估胚胎的最终标准的特征空间。
使用低质量的 2D 灰度图像进行的实验结果证明了所提出技术的有效性，并为预测胚胎质量的新型自动化方法提供了证据。
