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.

中文翻译：

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ImageJ 生态系统：用于图像可视化、处理和分析的开源软件


几十年来，生物学家一直依靠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，导致多维数据集更大，成像软件必须适应。 ImageJ 是一个开源图像分析软件平台，主要由参与和协作的用户和开发人员社区推动，为研究人员提供各种图像分析应用程序。程序员和用户之间的密切合作带来了适应图像分析新挑战的适应性，满足 ImageJ 不同用户群的需求。 ImageJ 由许多组件组成，其中一些主要与开发人员相关，以及大量以用户为中心的插件。它有多种形式，包括广泛使用的斐济发行版。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特征，并重点介绍了 ImageJ 如何通过近年来的新插件和工具来应对成像技术的进步。这些插件和工具的开发是为了满足用户在多个领域的需求，例如大型复杂数据集中生物实体的可视化、分割和跟踪。此外，ImageJ 中还添加了新的深度学习功能，反映了生物图像分析社区向利用人工智能的转变。 ImageJ2 项目对 ImageJ 核心带来的深刻架构变化促进了这些新工具的发展。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。