Institut de Biologie Structurale, Université Grenoble Alpes, CEA, CNRS, IBS, Grenoble, France.
Department of Chemistry, Umeå University, Umeå, Sweden.
PLoS Biol. 2024 Apr 30;22(4):e3002447. doi: 10.1371/journal.pbio.3002447. eCollection 2024 Apr.
Powerful, workflow-agnostic and interactive visualisation is essential for the ad hoc, human-in-the-loop workflows typical of cryo-electron tomography (cryo-ET). While several tools exist for visualisation and annotation of cryo-ET data, they are often integrated as part of monolithic processing pipelines, or focused on a specific task and offering limited reusability and extensibility. With each software suite presenting its own pros and cons and tools tailored to address specific challenges, seamless integration between available pipelines is often a difficult task. As part of the effort to enable such flexibility and move the software ecosystem towards a more collaborative and modular approach, we developed blik, an open-source napari plugin for visualisation and annotation of cryo-ET data (source code: https://github.com/brisvag/blik). blik offers fast, interactive, and user-friendly 3D visualisation thanks to napari, and is built with extensibility and modularity at the core. Data is handled and exposed through well-established scientific Python libraries such as numpy arrays and pandas dataframes. Reusable components (such as data structures, file read/write, and annotation tools) are developed as independent Python libraries to encourage reuse and community contribution. By easily integrating with established image analysis tools-even outside of the cryo-ET world-blik provides a versatile platform for interacting with cryo-ET data. On top of core visualisation features-interactive and simultaneous visualisation of tomograms, particle picks, and segmentations-blik provides an interface for interactive tools such as manual, surface-based and filament-based particle picking, and image segmentation, as well as simple filtering tools. Additional self-contained napari plugins developed as part of this work also implement interactive plotting and selection based on particle features, and label interpolation for easier segmentation. Finally, we highlight the differences with existing software and showcase blik's applicability in biological research.
强大、与工作流程无关且交互式的可视化对于冷冻电子断层扫描（cryo-ET）中典型的即席、人机交互工作流程至关重要。虽然有几种工具可用于可视化和注释 cryo-ET 数据，但它们通常作为整体处理管道的一部分集成，或者专注于特定任务，提供有限的可重用性和可扩展性。每个软件套件都有其自身的优缺点，并且工具针对特定挑战进行了定制，因此，在可用管道之间实现无缝集成通常是一项艰巨的任务。作为使这种灵活性成为可能并使软件生态系统朝着更具协作性和模块化方法发展的努力的一部分，我们开发了 blik，这是一个用于可视化和注释 cryo-ET 数据的开源 napari 插件（源代码：https://github.com/brisvag/blik）。blik 借助 napari 提供快速、交互和用户友好的 3D 可视化，并且以可扩展性和模块化为核心构建。数据通过成熟的科学 Python 库（如 numpy 数组和 pandas 数据框）进行处理和公开。可重用组件（如数据结构、文件读写和注释工具）作为独立的 Python 库开发，以鼓励重用和社区贡献。通过与已建立的图像分析工具轻松集成-甚至在 cryo-ET 领域之外-blik 为与 cryo-ET 数据交互提供了一个通用平台。除了核心可视化功能（交互式和同时可视化断层扫描、粒子选择和分割）之外，blik 还提供了用于交互式工具的接口，例如手动、基于表面和基于纤维的粒子选择以及图像分割，以及简单的过滤工具。作为这项工作的一部分开发的其他独立的 napari 插件还实现了基于粒子特征的交互式绘图和选择，以及标签插值，以方便分割。最后，我们强调了与现有软件的区别，并展示了 blik 在生物研究中的适用性。
