Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal
for advanced surgical systems. Existing methods either compromise on rendering quality or …

Deep implicit shape models have become popular in the computer vision community at
large but less so for biomedical applications. This is in part because large training …

The widespread availability of publicly accessible medical images has significantly
propelled advancements in various research and clinical fields. Nonetheless, concerns …

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

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 …

Estimating the shape and motion state of the myocardium is essential in diagnosing
cardiovascular diseases. However, cine magnetic resonance (CMR) imaging is dominated …

Modern medical image segmentation methods primarily use discrete representations in the
form of rasterized masks to learn features and generate predictions. Although effective, this …

Medical images are generally acquired with limited field-of-view (FOV), which could lead to
incomplete regions of interest (ROI), and thus impose a great challenge on medical image …

Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the
analysis of cardiac morphology and function and—more generally—diagnosis of …

Abstract Statistical Shape Modelling (SSM) is an effective tool for quantitatively analyzing
anatomical populations. SSM has benefitted largely from advances in deep learning where …