Research into artificial intelligence (AI) has made tremendous progress over the past
decade. In particular, the AI-powered analysis of images and signals has reached human …

Myocarditis represents the entity of an inflamed myocardium and is a diagnostic challenge
caused by its heterogeneous presentation. Contemporary noninvasive evaluation of patients …

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …

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

Background: Convolutional neural network (CNN) based segmentation methods provide an
efficient and automated way for clinicians to assess the structure and function of the heart in …

Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …

Segmenting anatomical structures in medical images has been successfully addressed with
deep learning methods for a range of applications. However, this success is heavily …

Segmentation of cardiac anatomical structures in cardiac magnetic resonance images
(CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases …

Many studies on machine learning (ML) for computer-aided diagnosis have so far been
mostly restricted to high-quality research data. Clinical data warehouses, gathering routine …

Abstract Medical Image Segmentation is the process of segmenting and detecting
boundaries of anatomical structures in various types of 2D and 3D-medical images. The …