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 …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

This paper introduces a novel top-down representation approach for deformable image
registration which estimates the deformation field by capturing various short-and long-range …

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for
the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current …

Diffeomorphic image registration, offering smooth transformation and topology preservation,
is required in many medical image analysis tasks. Traditional methods impose certain …

Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural
abnormalities, often requiring customized treatment plans for individual patients …

Image registration is an essential step in many medical image analysis tasks. Traditional
methods for image registration are primarily optimization-driven, finding the optimal …

Advances in self-supervised learning, especially in contrastive learning, have drawn
attention to investigating these techniques in providing effective visual representations from …

We propose a novel neural deformable model (NDM) targeting at the reconstruction and
modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic …

Neural Radiance Field (NeRF) shows dramatic results in synthesising novel views.
However, existing controllable and editable NeRF methods are still incapable of both fine …