biv-me is a repository for creating personalised 3D+t biventricular meshes from cardiac magnetic resonance imaging. Developed and maintained by Joshua Dillon and Charlène Mauger.

RadImageNet, a pre-trained convolutional neural networks trained solely from medical imaging to be used as the basis of transfer learning for medical imaging applications.

Python utilities for medical image and surface-based analysis

Scripts for visualization of medical images and 3D structures like meshes

Toolbox for Differential Geometry on Triangle and Tetrahedra Meshes (FEM, Laplace, Poisson, Heat Flow ...)

A set of quality control scripts for FreeSurfer- and FastSurfer-processed structural MRI data

Unsupervised Learning for Image Registration

Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

PyTorch extensions for fast R&D prototyping and Kaggle farming

C++ implementation for computing occupancy grids and signed distance functions (SDFs) from watertight meshes.

A scikit-learn compatible neural network library that wraps PyTorch

Geometric loss functions between point clouds, images and volumes

PyTorch implementation of FastSurferCNN

A framework for data augmentation for 2D and 3D image classification and segmentation

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks

A memory-efficient implementation of DenseNets

A collection of losses not part of pytorch standard library particularly useful for segmentation task

PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty