Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …

We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …

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

We propose a method based on deep learning to perform cardiac segmentation on short
axis Magnetic resonance imaging stacks iteratively from the top slice (around the base) to …

Convolutional neural networks have been widely used in medical image segmentation. The
amount of training data strongly determines the overall performance. Most approaches are …

The measurement of root growth over time, without destructive excavation from soil is
important to understanding the development of plants, communities and ecosystems …

Background The trend towards large-scale studies including population imaging poses new
challenges in terms of quality control (QC). This is a particular issue when automatic …

Automated brain tumor segmentation from magnetic resonance (MR) images plays a crucial
role in precise diagnosis and treatment monitoring in brain tumor care. Leveraging the Brain …

Multi-organ segmentation is one of most successful applications of deep learning in medical
image analysis. Deep convolutional neural nets (CNNs) have shown great promise in …