During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.

在胚胎发生过程中，细胞反复分裂并动态改变其在三维 (3D) 空间中的位置。获取细胞 3D 位置的稳健且准确的算法将有助于揭示胚胎发生机制。为了从时间序列 3D 显微图像中获取胚胎发生的定量标准，应用了分割等图像处理算法。由于胚胎中的细胞相当拥挤，因此需要一种详细而准确地分割单个细胞的算法。为了从活细胞的时间序列 3D 荧光显微图像中量化每个细胞的核区域，我们开发了 QCANet，一种基于卷积神经网络的 3D 荧光生物图像分割算法。我们证明了 QCANet 的性能优于 3D Mask R-CNN，后者目前被认为是实例分割的最佳算法。我们证明 QCANet 不仅可以应用于小鼠胚胎的发育，还可以应用于其他两个模型物种的胚胎的发育。使用 QCANet，我们能够从 11 个早期小鼠胚胎中提取胚胎发生的几个定量标准。我们表明提取的标准可用于评估个体胚胎之间的差异。这项研究有助于开发基于提取的定量标准评估胚胎发生的基本方法。