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 …

Cardiovascular imaging is going to change substantially in the next decade, fueled by the
deep learning revolution. For medical professionals, it is important to keep track of these …

Delineation of the cardiac structures from 2D echocardiographic images is a common
clinical task to establish a diagnosis. Over the past decades, the automation of this task has …

Convolutional Neural Networks (CNNs) have been recently employed to solve problems
from both the computer vision and medical image analysis fields. Despite their popularity …

Incorporation of prior knowledge about organ shape and location is key to improve
performance of image analysis approaches. In particular, priors can be useful in cases …

Image segmentation is a prerequisite for image processing. There are many methods for
image segmentation, and as a result, a great number of methods for evaluating …

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in
medicine, to assist in data classification, novel disease phenotyping and complex decision …

International challenges have become the standard for validation of biomedical image
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …

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 …

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular
disease. However, interpretation remains largely reliant on the subjective expertise of the …