Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France.
Department of Biological and Molecular Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK.
J Cell Sci. 2022 Apr 1;135(7). doi: 10.1242/jcs.258986. Epub 2022 Apr 14.
For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on the availability of these methods. We highlight why the quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.
在过去的一个世纪里，细胞核一直是细胞生物学广泛研究的焦点。然而，关于其形状和大小如何在发育过程中、在不同组织中或在疾病和衰老过程中得到调节，仍有许多问题尚未得到解答。为了跟踪这些变化，显微镜一直是首选工具。通过提供可用于将定性图像转化为定量参数的计算工具，图像分析彻底改变了这一研究领域。许多工具被设计用于在 2D 和最终在 3D 中限定物体，以定义它们的形状、数量或在核空间中的位置。如今，该领域由深度学习方法驱动，其中大多数方法都利用卷积神经网络。这些技术在使用大型数据集和强大的计算机图形卡进行训练时，非常适合生物医学图像。为了向细胞生物学家推广这些创新和有前途的方法，本综述总结了深度学习的主要概念和术语。特别强调了这些方法的可用性。我们强调了为什么训练图像数据集的质量和特征很重要，以及在哪里可以找到它们，以及如何创建、存储和共享图像数据集。最后，我们描述了非常适合细胞核 3D 分析的深度学习方法，并根据其对生物学家的可用性对它们进行分类。在 150 多种已发表的方法中，我们确定了不到 12 种生物学家可以使用的方法，并解释了原因。基于这一经验，我们提出了与生物学家共享深度学习方法的最佳实践。
