The use of models of stem cell differentiation to trophoblastic cells provides an effective perspective for understanding the early molecular events in the establishment and maintenance of human pregnancy. In combination with the newly developed deep learning technology, the automated identification of this process can greatly accelerate the contribution to relevant knowledge. Based on the transfer learning technique, we used a convolutional neural network to distinguish the microscopic images of Embryonic stem cells (ESCs) from differentiated trophoblast -like cells (TBL). To tackle the problem of insufficient training data, the strategies of data augmentation were used. The results showed that the convolutional neural network could successfully recognize trophoblast cells and stem cells automatically, but could not distinguish TBL from the immortalized trophoblast cell lines in vitro (JEG-3 and HTR8-SVneo). We compare the recognition effect of the commonly used convolutional neural network, including DenseNet, VGG16, VGG19, InceptionV3, and Xception. This study extends the deep learning technique to trophoblast cell phenotype classification and paves the way for automatic bright-field microscopic image analysis of trophoblast cells in the future.

使用干细胞分化为滋养层细胞的模型为理解人类妊娠建立和维持中的早期分子事件提供了有效的视角。结合新开发的深度学习技术，这一过程的自动化识别可以大大加快对相关知识的贡献。基于转移学习技术，我们使用卷积神经网络来区分胚胎干细胞 (ESC) 和分化的滋养层样细胞 (TBL) 的显微图像。为了解决训练数据不足的问题，使用了数据增强策略。结果表明，卷积神经网络可以成功自动识别滋养层细胞和干细胞，但无法区分 TBL 与体外永生化滋养层细胞系（JEG-3 和 HTR8-SVneo）。我们比较了常用的卷积神经网络的识别效果，包括DenseNet、VGG16、VGG19、InceptionV3和Xception。该研究将深度学习技术扩展到滋养层细胞表型分类，并为未来滋养层细胞的自动明场显微图像分析铺平了道路。