Selection of the best quality embryo is the key for a faithful implantation in in vitro fertilization (IVF) practice. However, the process of evaluating numerous images captured by time‐lapse imaging (TLI) system is time‐consuming and some important features cannot be recognized by naked eyes. Convolutional neural network (CNN) is used in medical imaging yet in IVF. The study aims to apply CNN on day‐one human embryo TLI. We first presented CNN algorithm for day‐one human embryo segmentation on three distinct features: zona pellucida (ZP), cytoplasm and pronucleus (PN). We tested the CNN performance compared side‐by‐side with manual labelling by clinical embryologist, then measured the segmented day‐one human embryo parameters and compared them with literature reported values. The precisions of segmentation were that cytoplasm over 97%, PN over 84% and ZP around 80%. For the morphometrics data of cytoplasm, ZP and PN, the results were comparable with those reported in literatures, which showed high reproducibility and consistency. The CNN system provides fast and stable analytical outcome to improve work efficiency in IVF setting. To conclude, our CNN system is potential to be applied in practice for day‐one human embryo segmentation as a robust tool with high precision, reproducibility and speed.

中文翻译：

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卷积神经网络在体外受精早期人类胚胎分割中的应用

选择最优质的胚胎是体外忠实植入的关键受精（IVF）实践。然而，评估由延时成像（TLI）系统捕获的大量图像的过程非常耗时，并且肉眼无法识别一些重要特征。卷积神经网络 (CNN) 用于医学成像，目前还用于 IVF。该研究旨在将 CNN 应用于第一天的人类胚胎 TLI。我们首先在三个不同的特征上提出了用于第一天人类胚胎分割的 CNN 算法：透明带 (ZP)、细胞质和原核 (PN)。我们测试了 CNN 的性能，并与临床胚胎学家的手动标记进行了并排比较，然后测量了分段的第一天人类胚胎参数，并将它们与文献报道的值进行了比较。分割精度为细胞质超过97%，PN超过84%，ZP在80%左右。对于细胞质、ZP 和 PN 的形态计量学数据，结果与文献报道的结果具有可比性，具有较高的重现性和一致性。CNN 系统提供快速稳定的分析结果，以提高 IVF 设置的工作效率。总而言之，我们的 CNN 系统作为一种具有高精度、可重复性和速度的强大工具，有可能在实践中应用于人类胚胎分割的第一天。