This report presents a generic image registration benchmark with automatic evaluation using landmark annotations.
However, a major drawback of these methods is that they require a large number of annotated training images.
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks.
We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration.
In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets.
This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans.
In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model.
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).