论文标题
IVF的人类胚胎关键形态特征的自动测量
Automated Measurements of Key Morphological Features of Human Embryos for IVF
论文作者
论文摘要
临床体外受精（IVF）的主要挑战是选择最高质量的胚胎以转移给患者，以期达到怀孕。延时显微镜为临床医生提供了大量用于选择胚胎的信息。但是，目前正在手动分析产生的胚胎电影，这是耗时且主观的。在这里，我们使用五个卷积神经网络（CNN）的机器学习管道自动提取人类胚胎的延时显微镜。我们的管道包括（1）胚胎区域的语义分割，（2）碎片严重程度的回归预测，（3）发育阶段的分类以及（4）细胞的对象实例分割（4）细胞和（5）pronuclei。我们的方法大大加快了对可能有助于胚胎选择的定量，生物学相关特征的测量。
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy. Time-lapse microscopy provides clinicians with a wealth of information for selecting embryos. However, the resulting movies of embryos are currently analyzed manually, which is time consuming and subjective. Here, we automate feature extraction of time-lapse microscopy of human embryos with a machine-learning pipeline of five convolutional neural networks (CNNs). Our pipeline consists of (1) semantic segmentation of the regions of the embryo, (2) regression predictions of fragment severity, (3) classification of the developmental stage, and object instance segmentation of (4) cells and (5) pronuclei. Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.
