Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

Medical image analysis is performed by analyzing images obtained by medical imaging
systems to solve clinical problems. The purpose is to extract effective information and …

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

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 …

Abstract Machine learning (ML) is making a dramatic impact on cardiovascular magnetic
resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR …

Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic
resonance (CMR) image segmentation. However, most approaches have focused on …

Typically, a medical image offers spatial information on the anatomy (and pathology)
modulated by imaging specific characteristics. Many imaging modalities including Magnetic …

In ophthalmologic practice, retinal images are routinely obtained to diagnose and monitor
primary eye diseases and systemic conditions affecting the eye, such as diabetic …

Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a
crucial role in 3D breast reconstruction which can assist radiologists in surgery planning …