neuroimaging

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Deep Learning Toolkit for Medical Image Analysis

Multi-platform, free open source software for visualization and image computing.

Machine learning for NeuroImaging in Python

Multi-platform, free open source software for visualization and image computing.

The MATLAB toolbox for MEG, EEG and iEEG analysis

Workflows and interfaces for neuroimaging packages

dcm2nii DICOM to NIfTI converter: compiled versions available from NITRC

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

Python package to access a cacophony of neuro-imaging file formats

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

Pycortex is a python-based toolkit for surface visualization of fMRI data

Deep learning software to decode EEG, ECG or MEG signals

Neuroimaging analysis and visualization suite

Brain Imaging Analysis Kit

normalize the intensities of various MR image modalities

Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain

Brain Imaging Data Structure (BIDS) Specification

Software platform for clinical neuroimaging studies

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"