CVPR

GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency

We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly …

Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment

Atlas building and image registration are important tasks for medical image analysis. Once one or multiple atlases from an image population have been constructed, commonly (1) images are warped into an atlas space to study intra-subject or …

Deep Decomposition for Stochastic Normal-Abnormal Transport

Advection-diffusion equations describe a large family of natural transport processes, e.g., fluid flow, heat transfer, and wind transport. They are also used for optical flow and perfusion imaging computations. We develop a machine learning model, …

Discovering Hidden Physics Behind Transport Dynamics

Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most com- monly by injecting a tracer. An advection-diffusion equation is widely used to …

Metric Learning for Image Registration

Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for …

Networks for Joint Affine and Non-Parametric Image Registration

We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity …

AGA: Attribute-Guided Augmentation

We consider the problem of data augmentation, ie, generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that …

One-Shot Learning of Scene Locations via Feature Trajectory Transfer

The appearance of (outdoor) scenes changes considerably with the strength of certain transient attributes, such as" rainy"," dark" or" sunny". Obviously, this also affects the representation of an image in feature space, eg, as activations at a …

Registration of Developmental Image Sequences with Missing Data

Longitudinal image registration is commonly used to establish spatial correspondence between images when investigating temporal changes in brain morphology. Most image registration methods have been developed to align images that are similar in …

Scene Parsing with Object Instances and Occlusion Ordering

This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a …