Tensorflow 2.0/Keras implementation of MR(A)I workshop material

This repository contains my code for modelling diffusion of water molecules inside the brain. The project involved implementing Spherical Mean Technique to estimate per-voxel diffusion coefficient from diffusion MRI data (High Angular Resolution Diffusion Imaging).

UNDER CONSTRUCTION: Mouse brain morphometry analysis pipeline for MRI imaging data

Teletext animation of my MRI brain scan on BBC Micro computer

Software for a preclinical traumatic brain injury study

Project for UCSF 265

A Matlab toolbox for examining the quality of structural (SNR) and functional (tSNR, SFNR) MRI

Predicting risky behavior in structural brain volume using the UK Biobank

Generation missing MRI using GANs - master thesis from Politechnic of Milan

A toolbox for NIfTI and Analyze medical image visualization, editing, and 3D rendering

It displays MRI in 2D and 3D multi-slice views, with easy search and navigation with a 3D mouse.

This is a set of tools associated with automatically segmenting tractography datasets using a combination of two complementary approaches: streamlines included in a fascicle model are identified based on 1) anatomical connectivity priors based on Freesurfer-derived regions of interest (ROIs) in the subject’s native space and 2) shape priors base…

Lesion Segmentation Tool by Paul Schmidt (https://www.applied-statistics.de/lst.html)

raw metadata files, scripts and derived files for 'Mindfulness related changes in grey matter: A Systematic Review and Meta-analysis'

A brain MRI segmentation tool that provides accurate robust segmentation of problematic brain regions across the neurodegenerative spectrum. The methodology is generalisable to perform well with the typical variance in MRI acquisition parameters and other factors that influence image contrast.

This Machine Learning course project is on 3D MRI image analysis for Alzheimer's disease prediction. Transfer Learning is used for classification and analysis of images.

The following is a new architecture for robust segmentation. It may perform better than a U-Net :) for binary segmentation. I will update the code when I have some spare time within the next month. However you can simply read this one and will soon notice the pattern after a bit

Official implementation of the paper "Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI" accepted to the MICCAI 2021 BrainLes workshop

Takes in multiple Nifti files with masks and converts them into training and validation datasets for use in AI/ML training

A workflow to allow Freesurfer recon-all to run on brain image with gliomas