The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.

以高通量在微观水平上评估精子的能力将对辅助生殖技术（ARTs）有用，因为它可以特定地选择精子细胞进行体外受精（IVF）。在生殖医学中，固有成像和外部造影剂之间的权衡尤为严重。荧光标记的使用启用了新的细胞分选策略，并为发育生物学提供了新的见解。然而，对于常规的临床手术而言，使用外在性对比剂通常过于侵入。提出有关细胞活力的问题，特别是对于单细胞选择，临床医生更喜欢以相衬，微分干涉或霍夫曼调制对比的形式进行内在对比。尽管这类仪器是非破坏性的，产生的图像缺乏特异性。在这项工作中，我们提供了一个模板，通过将高灵敏度的相位成像与深度学习相结合来规避细胞活力和特异性之间的折衷。为了向无标签图像引入特异性，我们训练了深度卷积神经网络以在定量相位图上执行语义分割。这种方法是具有计算特异性（PICS）的阶段成像的一种形式，它使我们能够有效地分析成千上万的精子细胞，并确定干物质含量与人工繁殖结果之间的相关性。具体来说，我们发现细胞的头部，中段和尾部之间的干物质含量比可以预测合子分裂和胚胎胚泡形成的成功百分比。我们提供了一个模板，可通过将高灵敏度的相位成像与深度学习相结合来规避细胞活力与特异性之间的折衷。为了向无标签图像引入特异性，我们训练了深度卷积神经网络以在定量相位图上执行语义分割。这种方法是具有计算特异性（PICS）的阶段成像的一种形式，它使我们能够有效地分析成千上万的精子细胞，并确定干物质含量与人工繁殖结果之间的相关性。具体来说，我们发现细胞的头部，中段和尾部之间的干物质含量比可以预测合子分裂和胚胎胚泡形成的成功百分比。我们提供了一个模板，通过将高灵敏度的相位成像与深度学习相结合来规避细胞活力和特异性之间的折衷。为了向无标签图像引入特异性，我们训练了深度卷积神经网络在定量相位图上执行语义分割。这种方法是具有计算特异性（PICS）的阶段成像的一种形式，使我们能够有效地分析成千上万的精子细胞，并确定干物质含量与人工繁殖结果之间的相关性。具体来说，我们发现细胞的头部，中段和尾部之间的干物质含量比可以预测合子分裂和胚胎胚泡形成的成功百分比。