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 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 …

International challenges have become the de facto standard for comparative assessment of
image analysis algorithms. Although segmentation is the most widely investigated medical …

Semantic segmentation of medical images aims to associate a pixel with a label in a medical
image without human initialization. The success of semantic segmentation algorithms is …

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

The performance on deep learning is significantly affected by volume of training data.
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for …

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several
slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from …

Segmentation of medical images, particularly late gadolinium-enhanced magnetic
resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first …

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 …

Abstract Knowledge of whole heart anatomy is a prerequisite for many clinical applications.
Whole heart segmentation (WHS), which delineates substructures of the heart, can be very …