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Front Neuroinform. 2017 May 29;11:35. doi: 10.3389/fninf.2017.00035. eCollection 2017.

ATPP: A Pipeline for Automatic Tractography-Based Brain Parcellation.

Li H1,2,3, Fan L1,3, Zhuo J1, Wang J4, Zhang Y1,2, Yang Z1, Jiang T1,2,3,4,5,6.

Author information

1
Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.
2
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China.
3
University of Chinese Academy of SciencesBeijing, China.
4
Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China.
5
CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of SciencesBeijing, China.
6
Queensland Brain Institute, The University of QueenslandBrisbane, QLD, Australia.

Abstract

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.

KEYWORDS:

brain atlas; diffusion tractography; neuroimaging pipeline; parallel computing; parcellation

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