Generate custom Docker and Singularity images, and minimize existing containers
A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning
The project is used to do preprocessing on brain MR images by using Nipype.
The Medical Image Analysis Laboratory Super-Resolution ToolKit (MIALSRTK) consists of a set of C++ and Python processing and workflow tools necessary to perform motion-robust super-resolution fetal MRI reconstruction in the BIDS Apps framework.
Neuropycon package of functions for electrophysiology analysis, can be used from graphpype and nipype
Preprocessing Pipelines for EEG (MNE-python), fMRI (nipype), MEG (MNE-python/autoreject) data
PNH segmentation pipelines based on nipype
Open-source eddy-current and head-motion correction for dMRI.
Reusable neuroimaging pipelines using nipype
A Dockerfile to create a Ubuntu docker for neuroimaging
Portable, modular, reusable, and reproducible processing pipeline software for fetal brain MRI super-resolution
Pypelines Utilizing a Modular Inventory
Some pipelines implemented with nipype.
PUMI: neuroimaging Pipelines Using Modular workflow Integration
Nipype workflow to generate fieldmaps from EPI acquisitions with differing phase-encoding directions
Nipype interface(s) wrapping the fsl_anat command line tool
The basic structural and diffusion MRI registration with pre-processing pipeline in Python.
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