Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical
cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac …

Statistical shape models (SSMs) have by now been firmly established as a robust tool for
segmentation of medical images. While 2D models have been in use since the early 1990s …

While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D
medical image segmentation, it is still a difficult task for CNNs to segment important organs …

Purpose To develop a deep learning–based method for fully automated quantification of left
ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a …

For the last 15 years, Magnetic Resonance Imaging (MRI) has become a reference
examination for cardiac morphology, function and perfusion in humans. Yet, due to the …

Automatic image processing methods are a pre-requisite to efficiently analyze the large
amount of image data produced by computed tomography (CT) scanners during cardiac …

We present a framework for the analysis of short axis cardiac MRI, using statistical models of
shape and appearance. The framework integrates temporal and structural constraints and …

Purpose This study aims to accurately segment the right ventricle (RV) from cardiac MRI
using a fully automatic learning‐based method. Methods The proposed method uses deep …

Magnetic resonance (MR) imaging has become a routine modality for the determination of
patient cardiac morphology. The extraction of this information can be important for the …

Heart disease is the leading cause of death in the modern world. Cardiac imaging is
routinely applied for assessment and diagnosis of cardiac diseases. Computerized image …