In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …

Deep learning-based segmentation methods provide an effective and automated way for
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …

We present a parametric physics-informed neural network for the simulation of personalised
left-ventricular biomechanics. The neural network is constrained to the biophysical problem …

Most image segmentation algorithms are trained on binary masks formulated as a
classification task per pixel. However, in applications such as medical imaging, this “black …

Synthesis of a large set of high-quality medical images with variability in anatomical
representation and image appearance has the potential to provide solutions for tackling the …

Cardiac magnetic resonance (CMR) image segmentation is an integral step in the analysis
of cardiac function and diagnosis of heart related diseases. While recent deep learning …

Due to privacy issues and limited amount of publicly available labeled datasets in the
domain of medical imaging, we propose an image generation pipeline to synthesize 3D …

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust
learning-based models, but obtaining such data can be challenging in many medical image …

Purpose Sparsity of annotated data is a major limitation in medical image processing tasks
such as registration. Registered multimodal image data are essential for the diagnosis of …

Generative adversarial networks (GANs) have provided promising data enrichment solutions
by synthesizing high-fidelity images. However, generating large sets of labeled images with …