Title: macapype: segmentation to mesh pipeline.
Project lead and collaborators: David Meunier @davidmeunier79 Bastien Cagna @BastienCagna Kep Kee Loh @kepkeeloh
Registered Brainhack Global 2020 Event: BrainHack Marseille 2-4 Dec 2020:
Description: Macapype is an open source python package based on the nipype framework. Dedicated to the processing of NHP MRI data, Macapype brings together existing tools from popular neuroimaging softwares (e.g. FSL, ANTs, SPM, AFNI etc), and the wraps of specialised scripts for NHP MRI data processing. Currently, Macapype also provides predefined pipelines for the preprocessing, brain extraction, and segmentation of NHP MRI data that are easy to use, and customizable to various input file types (T1w or T1w/T2w). These segmentation pipelines have been successfully adapted to MR images from various NHP species, including the macaques, baboons and marmosets.
Goals for Brainhack Marseille In this brainhack, we would like to expand the existing pipelines of Macapype to allow the generation of surface meshes following the segmentation of the MR images. So far, macapype users have been performing the above process (i.e. surface mesh generation) by first, using a set of customised scripts to import Macapype-generated segmentations into Brainvisa, and second, to manually (point-and-click) generate surface meshes via the Morphologist Toolbox in Brainvisa. We aim to incorporate these two steps as an additional module in Macapype, which will enable the generation of surface meshes directly from the segmentations produced by Macapype. The final result would be a powerful pipeline that takes raw NHP structural MR scans as input, and generating tissue segmentation masks and surface meshes as outputs.
Tools/Software/Methods to Use: Related softwares : brainvisa, freesurfer, nipype
Type of project: coding_methods, data_management, #documentation, method_development, #pipeline_development, tutorial_recording, visualization
Project development status: 0_concept_no_content, 1_basic structure, #2_releases_existing
Topic of the projet: Bayesian_approaches, causality, connectome, data_visualisation, deep_learning, diffusion, diversity_inclusivity_equality, EEG_EventRelatedResponseModelling, EEG_source_modelling, Granger_causality, hypothesis_testing, ICA, information_theory, machine_learning, #MR_methodologies, neural_decoding, neural_encoding, neural_networks, PCA, physiology, reinforcement_learning, reproducible_scientific_methods, single_neuron_models, statistical_modelling, systems_neuroscience, tractography
Tools used in the project: #AFNI, #ANTs, #BIDS, Brainstorm, #CPAC, Datalad, DIPY, FieldTrip, fMRIPrep, #Freesurfer, #FSL, Jupyter, MNE, MRtrix, #Nipype, NWB, #SPM
Tools skill level required to enter the project (more than one possible): #comfortable, #expert, #familiar, #no_skills_required
Programming language used in the project: no_programming_involved, C++, #containerization, #documentation, Java, Julia, #Matlab, #Python, R, shell_scripting, #Unix_command_line, Web, #workflows
Modalities involved in the project (if any): behavioral, DWI, ECG, ECOG, EEG, eye_tracking, fMRI, fNIRS, MEG, #MRI, PET, TDCS, TMS
Git skills reuired to enter the project (more than one possible): 0_no_git_skills, 1_commit_push, #2_branches_PRs, 3_continuous_integration
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