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.
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.
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.
This software uses a learning framework (YETI) building on an auto-encoder structure between 2D and 3D image time-series, which incorporates an advection-diffusion model to capture blood perfusion. To help with identifiability, the deep learning model is trained via simulated data from an advection-diffusion simulator.
Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervised learning tasks. However, recent works show that one can directly …
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. …
While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In this work, we show that the robustness of neural networks can be …
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.