This software allows for joint AtLAs builDing and Diffeomorphic regIstration learNing (Aladdin) with pairwise alignment. In contrast to existing atlas-building approaches it uses the atlas as a bridge and incorporates pairwise similarity measures between images which are related indirectly through their atlas registrations.


ICON (Inverse COnsistent RegistratioN) is a non-parametric deep learning registration approach which relies only on inverse consistency for regularity. As the regularization neither involves explicit smoothing or a penality on spatial derivatives no affine pre-registration is required.

OAI Analysis 2

This software contains open-source analysis approaches for the Osteoarthritis Initiative (OAI) magnetic resonance image (MRI) data. The analysis code is largely written in Python with the help of ITK and VTK for data I/O and mesh processing as well as PyTorch for the deep learning approaches for segmentation and registration.

Pediatric Airway Atlas

The project aims to provide an analytical for measuring the normality of children’s airways. We build an age-based atlas on multiple CT images of normal subjects. First, we use a segmentation model to extract the airway.


This software provides a general framework for point cloud/mesh registration based on robust optimal mass transport (robOT) / unbalanced optimal mass transport. It supports both optimization- and learning-based registration approaches. It also provides a general framework for deep prediction tasks, e.

Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer

Purpose: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high biologically effective radiation doses is essential to assess changes in organ-at-risk (OAR) …

ICON: Learning Regular Maps Through Inverse Consistency

Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformations. Such maps relate feature vectors, or map between feature spaces. …


EasyReg is an extension that builds on Mermaid, providing a simple interface to Mermaid and other popluar registration packages. The currently supported methods include Mermaid-optimization (i.e., optimization-based registration) and Mermaid-network (i.


Mermaid: iMagE Registration via autoMAtIc Differentiation Mermaid is a registration toolkit making use of automatic differentiation for rapid prototyping. It includes various image registration models. In particular, stationary velocity field models (both based on velocity fields and momentum fields), scalar vector momentum Large Displacement Diffeomorphic Metric Mapping (LDDMM) models as well as the more generalized Region-specific Diffeomorphic Metric Mapping model (RDMM).


VoteNet is a deep-learning-based label fusion strategy for multi-atlas segmentation (MAS) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. By selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct deep-learning (DL) segmentation approach.