Deformable image registration is a fundamental task in medical image processing. Among
its most important applications, one may cite: 1) multi-modality fusion, where information …

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 …

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 …

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI)
datasets is an essential step for calculation of clinical indices such as ventricular volume and …

Deep fully convolutional neural network (FCN) based architectures have shown great
potential in medical image segmentation. However, such architectures usually have millions …

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 …

This paper reviews ultrasound segmentation methods, in a broad sense, focusing on
techniques developed for medical B-mode ultrasound images. First, we present a review of …

We propose an automatic four-chamber heart segmentation system for the quantitative
functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics …

Micron-scale computed tomography (micro-CT) is an essential tool for phenotyping and for
elucidating diseases and their therapies. This work is focused on preclinical micro-CT …