Aix Marseille University, CNRS, IBDM, 13288 Marseille, France.
Max Planck Institute for Plant Breeding Research, 50829 Köln, Germany.
Development. 2020 Dec 23;147(24):dev194589. doi: 10.1242/dev.194589.
Epithelia are dynamic tissues that self-remodel during their development. During morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually involves extensive manual correction, even with semi-automated tools. Here, we present EPySeg, an open-source, coding-free software that uses deep learning to segment membrane-stained epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a Python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By substantially reducing human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.
上皮组织是具有自我重塑能力的动态组织。在形态发生过程中，上皮组织的组织尺度结构是通过组成组织的细胞的个体贡献总和获得的。因此，要理解任何形态发生事件，首先需要对其组成细胞进行彻底的分割。然而，即使使用半自动工具，这项任务通常也需要大量的手动修正。在这里，我们提出了 EPySeg，这是一个开源、无代码的软件，它使用深度学习来自动、高效地分割膜染色的上皮组织。EPySeg 带有一个简单的图形用户界面，可以作为一个 Python 包在本地计算机上使用，也可以通过 Google Colab 在没有兼容硬件的深度学习用户的云上使用。通过在图像分割中大大减少人工输入，EPySeg 加速并改善了所有发育生物学家对上皮组织的特征描述。
