Structural analysis

Overview

Details

AFNI @animal_warper

Authors : Daniel Glen, Paul Taylor, Adam Messinger, Benjamin Jung, Jakob Seidlitz
Description : Nonlinearly aligns an MRI dataset to a template. The reverse transformation can be used to produce a skullstripped (brain-only) version of the native scan, segmentation/atlas info in the native space, and surfaces for each atlas region. The computed transformations between the anatomical scan and the template is provided for use with FMRI pipeline tools like afni_proc.py.
Documentation : Online doc
Link : AFNI
Language : tcsh, python, C, AFNI
Publication : TBD
Communication : AFNI message board

BrainBox

Authors : Katja Heuer & Roberto Toro
Description : A Web application for visualising, annotating & segmenting 3D brain imaging data in real time, collaboratively.
Documentation : 3 min video
Link : https://brainbox.pasteur.fr
Language : JavaScript, HTML, CSS
Publication : Open Neuroimaging Laboratory
Communication : Mattermost or GitHub issues
Restrictions : Developed and tested in Chrome Browser

CIVET-macaque

Authors : Claude Lepage, Konrad Wagstyl, Ben Jung, Jakob Seidlitz, Caleb Sponheim, Leslie Ungerleider, Xindi Wang, Alan Evans, Adam Messinger
Description : Fully automated structural MRI pipeline using the NIH Macaque Template (NMT). Performs registration, segmentation, and surface reconstruction of T1-weighted anatomical scans. Provides quality control images and results.
Documentation : Github
Link : https://github.com/aces/CIVET_Full_Project
Language : C, minc, NIFTI/GIFTI. Binaries available on GitHub.
Publication : forthcoming
Communication : tbd
Restrictions : Cite the forthcoming paper

C-PAC: The Configurable Pipeline for the Analysis of Connectomes

Authors : Steven Giavasis, Cameron Craddock, Michael Milham
Description : The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is a configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. It is designed and tested for use with human data (all ages), as well as with non-human primate and rodent data.
Documentation : http://fcp-indi.github.io/
Link : Quick-Start
Language : Python
Publication : Craddock et al. (2013)
Communication : C-PAC Forum
Restrictions : None

Macapype

Authors : Bastien Cagna, David Meunier, Kep kee Loh, Julien Sein, & RĂ©gis Trapeau
Description : Python package for NHP anatomical MRI segmentation using Nipype.
Documentation : Online doc
Link : https://github.com/Macatools/macapype
Language : Python
Publication : -
Communication : Post issue on GitHub website
Restrictions : None

MR Comparative Anatomy Toolbox (MrCat)

Authors : Rogier B. Mars, Lennart Verhagen, and the members and collaborators of the Cognitive Neuroecology Lab
Description : A collection of tools for processing of multi-species neuroimaging data.
Documentation : Online doc
Link : www.neuroecologylab.org
Language : shell, matlab
Publication : Mars et al. 2016, among others
Communication : www.neuroecologylab.org
Restrictions : None

NHP-Freesurfer

Authors : Chris Klink
Description : Segmentation and surface generation of monkey brains using Freesurfer, the NMT template, and other tools. Jupyter Notebooks with documentation on how to generate surfaces and project results to it.
Documentation : Available in Jupyter notebook on GitHub
Link : GitHub link
Language : Jupyter notebooks with Shell code
Publication : -
Communication : GitHub profile
Restrictions : None

NHP-pycortex

Authors : Chris Klink
Description : Import surfaces generated with NHP-Freesurfer into a version of pycortex that is adapted for NHP use. This opens up the possibility of using pycortex to visualize fMRI results on the surface.
Documentation : Available in Jupyter notebook on GitHub
Link : GitHub link
Language : Jupyter notebooks with Python 3
Publication : -
Communication : GitHub profile
Restrictions : None

Precon_all

Authors : R. Austin Benn, Ting Xu
Description : precon_all is an anatomical surface reconstruction pipeline that can be used with Non-Human Primates, and other large animals including pigs, dogs, and potentially many more. The pipeline can be easily modified to work on most species with a reasonable T1 image by simply providing 5 masks. The pipeline provides both freesurfer and HCP compatible outputs in native image space. Group average surfaces and spherical registration templates can also be created within the precon_all framework.
Documentation : precon_all
Link : GitHub Link
Language : shell
Publication : In preparation
Communication : Email
Restrictions : None

PREEMACS

Authors : Pamela Garcia-Saldivar, Arun Garimella, Eduardo A. Garza-Villarreal, Felipe Mendez, Luis Concha and Hugo Merchant
Description : PREEMACS (pipeline for PREprocessing and Extraction of the MACaque brain Surface) is a pipeline to process raw structural images in order to obtain brain surfaces and cortical thickness, without requiring manual editing. PREEMACS has a modular design, with three modules running independently: Preprocessing, Quality Control and Brain Surface estimation based on FreeSurfer. To evaluate the generalizability of our method, we tested PREEMACS on three different datasets of NHP images: PRIME-DE, UNC-Wisconsin Database and INB-UNAM. Results showed accurate and robust automatic brain surface extraction in our INB-UNAM database and precise extraction in the UNC-Wisconsin and PRIME-DE databases for images that passed the quality control segment of our pipeline.
Documentation : PREEMACS
Link : GitHub Link
Language : python, shell and matlab
Publication : -
Communication : GitHub Profile
Restrictions : None

Reorient

Authors : Katja Heuer & Roberto Toro
Description : A Web tool for reorienting and cropping MRI data
Documentation : Readme in the GitHub repo
Link : GitHub Link
Language : JavaScript, HTML, CSS
Publication : Work in progress
Communication : GitHub issues
Restrictions : Developed and tested in Chrome Browser

Thresholdmann

Authors : Katja Heuer & Roberto Toro
Description : A Web tool for interactively creating adaptive thresholds to segment nifti images
Documentation : Readme in the GitHub repo
Link : GitHub Link
Language : JavaScript, HTML, CSS
Publication : Work in progress
Communication : GitHub issues
Restrictions : Developed and tested in Chrome Browser

UNet model for skull stripping and brain masks of anatomical images from PRIME-DE

Authors : Xindi Wang, Ting Xu
Description : The preprocessed brain masks of T1w images for all macaque monkeys from PRIME-DE. A convolutional network - UNet model was used to generate the brain mask for T1w images. The UNet model was initially trained in a large human sample and upgraded with a few macaque data. With a small macaque training sample (N=1-2), the UNet model achieves a decent performance of brain extraction with a minimal processing time (GPU: ~20s, CPU: 2-10 min).
Documentation : UNet model on PRIME-DE
Link : UNet model, code, preprocessed brain masks
Language : Python
Publication : In prepartion
Communication : GitHub profile
Restrictions : GNU