Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Abstract Machine learning (ML) applications in medical artificial intelligence (AI) systems
have shifted from traditional and statistical methods to increasing application of deep …

Deep learning models for semantic segmentation are able to learn powerful representations
for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible …

This chapter introduces two popular methods for unsupervised representation learning
using neural networks, namely autoencoders and variational autoencoders. Both methods …

Image-to-image translation has seen major advances in computer vision but can be difficult
to apply to medical images, where imaging artifacts and data scarcity degrade the …

This paper presents a novel predictive model, MetaMorph, for metamorphic registration of
images with appearance changes (ie, caused by brain tumors). In contrast to previous …

Deformable image registration is fundamental for many medical image analyses. A key
obstacle for accurate image registration lies in image appearance variations such as the …

In medical imaging, most of the image registration methods implicitly assume a one-to-one
correspondence between the source and target images (ie, diffeomorphism). However, this …

Image registration is fundamental in medical imaging applications, such as disease
progression analysis or radiation therapy planning. The primary objective of image …

We present an extension of the Metamorphosis algorithm to align images with different
topologies and/or appearances. We propose to restrict/limit the metamorphic intensity …