Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting
various anatomical structures in medical images but often suffer from relatively poor …

Deep learning (DL) approaches are state-of-the-art for many medical image segmentation
tasks. They offer a number of advantages: they can be trained for specific tasks …

Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to
diagnose heart disease. However, repetitive manual segmentation of these images requires …

We introduce a one-shot segmentation method to alleviate the burden of manual annotation
for medical images. The main idea is to treat one-shot segmentation as a classical atlas …

Existing image segmentation networks mainly leverage large-scale labeled datasets to
attain high accuracy. However, labeling medical images is very expensive since it requires …

In this work, we address the task of few-shot medical image segmentation (MIS) with a novel
proposed framework based on the learning registration to learn segmentation (LRLS) …

In medical image segmentation, supervised deep networks' success comes at the cost of
requiring abundant labeled data. While asking domain experts to annotate only one or a few …

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the
intensity similarity between target and atlas images. However, such similarity can be …

Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance
Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required …