Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …

Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

Medical image segmentation is an essential prerequisite for developing healthcare systems,
especially for disease diagnosis and treatment planning. On various medical image …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

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 …

In recent years, deep learning-based networks have achieved state-of-the-art performance
in medical image segmentation. Among the existing networks, U-Net has been successfully …

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

Despite recent progress of automatic medical image segmentation techniques, fully
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …

The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation
methods. However, the existing segmentation methods do not attempt to reduce HD directly …

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular
carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor …