In the field of reproductive health, a vital aspect for the detection of male fertility issues is the analysis of human semen quality. Two factors of importance are the morphology and motility of the sperm cells. While the former describes defects in different parts of a spermatozoon, the latter measures the efficient movement of cells. For many non-human species, so-called Computer-Aided Sperm Analysis systems work well for assessing these characteristics from microscopic video recordings but struggle with human sperm samples which generally show higher degrees of debris and dead spermatozoa, as well as lower overall sperm motility. Here, machine learning methods that harness large amounts of training data to extract salient features could support physicians with the detection of fertility issues or in vitro fertilisation procedures. In this work, the overall motility of given sperm samples is predicted with the help of a machine learning framework integrating unsupervised methods for feature extraction with downstream regression models. The models evaluated herein improve on the state-of-the-art for video-based sperm-motility prediction.
翻译:在生殖健康领域,发现男性生育问题的一个重要方面是分析人体精液质量,其中两个重要因素是精子细胞的形态学和运动能力,前者描述精子细胞不同部分的缺陷,后者衡量细胞的有效移动。对于许多非人类物种而言,所谓的计算机辅助Sperm分析系统在评估微小视频记录中的这些特征方面运作良好,但与一般显示碎片和死亡精子死亡程度较高的人类精子样本以及总体精子体积较低的人类精子样本进行挣扎。在这里,利用大量培训数据提取突出特征的机器学习方法可以支持医生检测生育问题或体外授精程序。在这项工作中,在将未经监控的特征提取方法与下游回归模型相结合的机器学习框架的帮助下,预测了特定精子样本的总体功能。这里评价的模型改进了视频精子运动预测的先进技术。