摘要翻译：
临床体外受精(IVF)的一个主要挑战是选择最高质量的胚胎移植给患者，以实现妊娠。延时显微镜为临床医生选择胚胎提供了丰富的信息。然而，胚胎的结果电影目前是人工分析，这是费时和主观的。在这里，我们用五个卷积神经网络（CNNs）组成的机器学习管道来自动提取人类胚胎延时显微镜的特征。我们的管道包括（1）胚胎区域的语义分割，(2)片段严重程度的回归预测，(3)发育阶段的分类，(4)细胞和（5）原核的对象实例分割。我们的方法大大加快了定量的、生物学相关的特征的测量，这些特征可能有助于胚胎选择。
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英文标题：
《Automated Measurements of Key Morphological Features of Human Embryos
  for IVF》
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作者：
Brian D. Leahy, Won-Dong Jang, Helen Y. Yang, Robbert Struyven,
  Donglai Wei, Zhe Sun, Kylie R. Lee, Charlotte Royston, Liz Cam, Yael Kalma,
  Foad Azem, Dalit Ben-Yosef, Hanspeter Pfister, Daniel Needleman
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最新提交年份：
2020
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2006.00067