Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin at Madison, Madison, Wisconsin, USA.
Morgridge Institute for Research, Madison, Wisconsin, USA.
Protein Sci. 2021 Jan;30(1):234-249. doi: 10.1002/pro.3993. Epub 2020 Nov 20.
For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.
几十年来，生物学家一直依赖软件来可视化和解释成像数据。随着获取图像的技术变得越来越复杂，产生的多维数据集也越来越大，成像软件必须进行相应的调整。ImageJ 是一个开源的图像分析软件平台，它为各种图像分析应用提供了帮助，主要是由活跃的、协作的用户和开发人员社区推动的。程序员和用户之间的密切合作导致了对新的图像分析挑战的适应，以满足 ImageJ 多样化用户群体的需求。ImageJ 由许多组件组成，其中一些主要与开发人员相关，还有大量以用户为中心的插件。它有多种形式，包括广泛使用的 Fiji 发行版。我们将整个 ImageJ 代码库和社区称为 ImageJ 生态系统。在这里，我们回顾了这个生态系统的核心功能，并强调了近年来 ImageJ 如何通过新的插件和工具来应对成像技术的进步。这些插件和工具是为了满足用户在可视化、分割和跟踪大型复杂数据集的生物实体等领域的需求而开发的。此外，ImageJ 中还增加了用于深度学习的新功能，反映出生物图像分析社区正在转向利用人工智能。ImageJ2 项目带来了对 ImageJ 核心的深刻架构更改，这些新工具也因此得以实现。因此，我们还讨论了 ImageJ2 对增强 ImageJ 生态系统中的多维图像处理和互操作性的贡献。
