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 …

The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …

Background Non-contrast head CT scan is the current standard for initial imaging of patients
with head trauma or stroke symptoms. We aimed to develop and validate a set of deep …

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …

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

Convolutional neural networks (CNN) have had unprecedented success in medical imaging
and, in particular, in medical image segmentation. However, despite the fact that …

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

In this paper, we present a novel convolutional neural network architecture to segment
images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed …

Medical image segmentation is a difficult but important task for many clinical operations such
as cardiac bi-ventricular volume estimation. More recently, there has been a shift to utilizing …

The medical field is creating large amount of data that physicians are unable to decipher
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …