Li Hai, Fan Lingzhong, Zhuo Junjie, Wang Jiaojian, Zhang Yu, Yang Zhengyi, Jiang Tianzi
Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China.
Front Neuroinform. 2017 May 29;11:35. doi: 10.3389/fninf.2017.00035. eCollection 2017.
There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation. In this paper, we present an integrated open source pipeline (https://www.nitrc.org/projects/atpp), named Automatic Tractography-based Parcellation Pipeline (ATPP) to realize the framework of parcellation with automatic processing and massive parallel computing. ATPP is developed to have a powerful and flexible command line version, taking multiple regions of interest as input, as well as a user-friendly graphical user interface version for parcellating single region of interest. We demonstrate the two versions by parcellating two brain regions, left precentral gyrus and middle frontal gyrus, on two independent datasets. In addition, ATPP has been successfully utilized and fully validated in a variety of brain regions and the human Brainnetome Atlas, showing the capacity to greatly facilitate brain parcellation.
长期以来,人们一直致力于根据微观结构、宏观结构或连接特征将大脑划分为不同区域,从而形成各种脑图谱。其中,基于连接性的划分受到了广泛关注,尤其是在过去二十年多模态磁共振成像取得显著进展的情况下。最近发布的脑网络组图谱就是这样一个遵循基于连接性划分框架的图谱。然而,在图谱构建过程中,高分辨率多模态MRI数据的海量以及耗时的计算带来了挑战,并且仍然缺乏专门用于划分的公开可用工具。在本文中,我们提出了一个集成的开源管道(https://www.nitrc.org/projects/atpp),名为基于自动纤维束成像的划分管道(ATPP),以通过自动处理和大规模并行计算实现划分框架。ATPP开发了功能强大且灵活的命令行版本,以多个感兴趣区域作为输入,还开发了用户友好的图形用户界面版本用于划分单个感兴趣区域。我们通过在两个独立数据集上对左中央前回和额中回两个脑区进行划分来展示这两个版本。此外,ATPP已在各种脑区和人类脑网络组图谱中成功应用并得到充分验证,显示出其极大地促进脑划分的能力。