Medical image segmentation is a paramount task for several clinical applications, namely for
the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries …

Besides network architecture, researchers have recently focused their attention on the loss
function for the Convolutional Neural Network-based medical image segmentation. The loss …

Medical image segmentation based on deep learning technics has been more and more
prevalent in recent years. The primary reasons lead to success of those methods are radical …

Image segmentation is vital to medical image analysis and clinical diagnosis. Recently,
convolutional neural networks (CNNs) have achieved tremendous success in this task …

Accurate segmentation of anatomic structure is an essential task for biomedical image
analysis. Recent popular object contours regression based segmentation methods have …

A U-Net based contour enhanced attention for medical image segmentation Welcome to Journal
of Graphics share: Superintended by China Association for Science and Technology Sponsored …

Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …

Deep convolutional networks recently made many breakthroughs in medical image
segmentation. Still, some anatomical artefacts may be observed in the segmentation results …

Image segmentation is a primary task in many medical applications. Recently, many deep
networks derived from U-Net has been extensively used in various medical image …

Today, deep convolutional neural networks (CNNs) have demonstrated state-of-the-art
performance for medical image segmentation, on various imaging modalities and tasks …