一个整合神经影像处理全流程的开源工具包，让研究者轻松操作各类影像数据。
CLABTOOLKIT: An Open-Source Toolkit for Routine Processing, Manipulation, and Visualization of Neuroimaging Data
- 将多种神经影像数据统一为可互操作的Python对象
- 支持从分割图生成连接组、投影标量到纤维束等19个模块功能
- 适合需要高效处理多模态神经影像的研究人员
神经影像研究需处理包括原始MRI体积、体积分割、皮层表面网格、纤维束和连接矩阵在内的异构数据，现有工具接口与格式不兼容，迫使研究者反复重写基础操作。我们提出CLABTOOLKIT，一个开源Python工具包，将这些操作整合为统一框架，通过五种核心数据结构（Parcellation, Surface, AnnotParcellation, Tractogram, Connectome）表示体积、表面和流线数据，并提供跨标准格式（如NIfTI, GIFTI, FreeSurfer注释, TCK/TRK）的一致加载、处理与导出方法，支持从分割图生成连接组及标量图投影至纤维束。配套模块涵盖BIDS数据管理、FreeSurfer集成、扩散MRI处理、形态分析、图论网络分析以及基于PyVista的GPU加速多面板可视化。工具包包含19个模块，分六层组织，提供13个面向对象类、234个方法和207个独立函数，采用基于JSON的配置系统实现工作流定制无需修改代码。相比现有库仅解决单一任务，CLABTOOLKIT在单框架内集成颜色与查找表管理、分割操作、多表面可视化与纤维束工具。兼容Python 3.9-3.12，采用Apache 2.0许可证。源码、文档与示例工作流见https://github.com/connectomicslab/clabtoolkit。
原文摘要 · Abstract (English)
Neuroimaging research requires manipulating heterogeneous data structures, including raw MRI volumes, volumetric parcellations, cortical surface meshes, tractograms, and connectivity matrices, across tools with incompatible interfaces and file formats, forcing researchers to repeatedly re-implement routine but technically demanding operations. We present CLABTOOLKIT, an open-source Python package that consolidates these operations into a single, coherent framework by representing volumetric, surface, and streamline data as interoperable Python objects. Five core data structures (Parcellation, Surface, AnnotParcellation, Tractogram, and Connectome) encapsulate common neuroanatomical entities and provide consistent methods for loading, processing, and exporting data across standard neuroimaging formats (e.g., NIfTI, GIFTI, FreeSurfer annotations, TCK/TRK), including connectome generation from a parcellation and scalar-map projection onto tractogram streamlines. Complementary modules support BIDS dataset management, FreeSurfer integration, diffusion MRI processing, morphometric analysis, graph-theoretical network analysis, and GPU-accelerated multi-panel visualization via PyVista. The toolkit comprises 19 modules organised into six layers, exposing 13 object-oriented classes with 234 methods and 207 standalone functions, and a JSON-based configuration system enables workflow customization without code changes. Unlike existing neuroimaging libraries, which typically address these tasks separately, CLABTOOLKIT combines color and lookup-table management, parcellation manipulation, multi-surface visualization, and tractography utilities within a single framework. CLABTOOLKIT is compatible with Python 3.9-3.12 and released under the Apache 2.0 license. Source code, documentation, and example workflows are available at https://github.com/connectomicslab/clabtoolkit.
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