We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks.
We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
Ranked #1 on Diffeomorphic Medical Image Registration on OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP (Dice metric)
We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
Ranked #1 on Diffeomorphic Medical Image Registration on CUMC12
In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets.
To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem.
Ranked #2 on BIRL on CIMA-10k