- Version: 0.8
- Project Maintainer: Qianqian Fang <q.fang at neu.edu>
- Contributors: See AUTHORS.txt for details
- Address:
- Department of Bioengineering
- Northeastern University
- 360 Huntington Ave, Boston, MA 02115
- License: GPL v3 or later, see LICENSE.txt
- URL: http://mcx.space/brain2mesh
The Brain2Mesh toolbox provides a streamlined matlab function to convert a segmented brain volumes and surfaces into a high-quality multi-layered tetrahedral brain/full head mesh.
The details of this toolbox is described in the paper listed in the Reference section.
This tool does not handle the segmentation of MRI scans, but examples of how commonly
encountered segmented datasets can be used to create meshes are available under the examples folder.
The Brain2Mesh toolbox is also extensively dependent on:
- Iso2Mesh toolbox (http://iso2mesh.sf.net), not included, download at https://github.com/fangq/iso2mesh
- MATLAB Image-Processing toolbox (such as
imfill,imdilate) intriangulation.m(by Adam Aitkenhead and Johannes Korsawe)
The function brain2mesh handles the conversion of segmented volumes into high-quality 3D meshes.
It takes an 4D array as input, with different assumptions as to the number of layers. Typically, the layers
are assumed to contain: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF), bone and scalp.
It also is able to handle inputs missing a bone segmentation, and missing bone+scalp segmentation.
Patient-specific segmentations can be done using common neuroimaging tools such as FSL, SPM, FreeSurfer and BrainSuite. There also exists series of available atlas databases that offer segmented volumes.
In a near future release, scripts will be made available to accomodate the combination of segmented surface meshes such as the ones produced in FreeSurfer and BrainSuite as part of the input data.
Another function brain1020 provides an automated interface to compute head landmarks (10-20/10-5 systems
or user-customizable divisions). Users can either interactively select 5 initial landmarks (nasion, inion,
left and right ear-lobes, i.e. LPA/RPA, and vertex, i.e. CZ), the function automatically computes all brain
landmarks on the scalp surface using user-specified density.
Your acknowledgement of Brain2Mesh in your publications or presentations would be greatly appreciated by the authors of this toolbox. The citation information can be found in the Introduction section.
If you use Brain2Mesh or Brain Mesh Library in your publications, the authors of this toolbox greatly appreciate if you can cite the below paper
- Anh Phong Tran†, Shijie Yan†, Qianqian Fang*, (2020) "Improving model-based fNIRS analysis using mesh-based anatomical and light-transport models," Neurophotonics, 7(1), 015008, URL: http://dx.doi.org/10.1117/1.NPh.7.1.015008
This project is funded by the National Institutes of Health (NIH) / National Institute of General Medical Sciences (NIGMS) under the grant number R01-GM114365, and NIH/NINID/NIBIB under the grant number R01-EB026998.
Copyright (c) 2016, Johannes Korsawe Copyright (c) 2013, Adam H. Aitkenhead All rights reserved.
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