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 …

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

In this paper, we introduce SpaER, a pioneering method for fetal motion tracking that
leverages equivariant filters and self-attention mechanisms to effectively learn spatio …

Large Deformation Diffeomorphic Metric Mapping (LDDMM) is a well-established
diffeomorphic registration method, a critical step for many medical imaging applications. Of …

Deep learning based methods provide efficient solutions to medical image registration,
including the challenging problem of diffeomorphic image registration. However, most …

In this paper, we introduce a novel end-to-end predictive model for efficient fetal motion
correction using deep neural networks. Diverging from conventional methods that estimate …

Eye diseases such as the chronic central serous chorioretinopathy are characterized by fluid
deposits that alter the retina and impair vision. These fluids occur at irregular intervals and …

Magnetic resonance imaging (MRI) technology has made it possible to observe the inside of
the brain in vivo, providing a window for researchers to better understand the processes of …

Deep learning based methods provide efficient solutions to medical image registration,
including the challenging problem of diffeomorphic image registration. However, most …