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).

Image Registration

Deformable image registration approaches via numerical optimization and deep learning.


Image analysis approaches for the quantitative analysis of osteoarthritis in the knee.

Pediatric Airway Analysis

Approaches to quantify pediatric airways.

Adversarial Data Augmentation via Deformation Statistics

Deep learning models have been successful in computer vision and medical image analysis. However, training these models frequently requires large labeled image sets whose creation is often very time and labor intensive, for example, in the context of …

Anatomical Data Augmentation via Fluid-based Image Registration

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our …

Fluid registration between lung CT and stationary chest tomosynthesis images

Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. However, while high-quality computed tomography (CT) images are often …

Votenet+: An Improved Deep Learning Label Fusion Method for Multi-Atlas Segmentation

In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network …

DeepAtlas: Joint Semi-supervised Learning of Image Registration and Segmentation

Deep convolutional neural networks (CNNs) are state-of-theart for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. …