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 …

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 …

Medical image segmentation is one of the most challenging tasks in medical image analysis
and widely developed for many clinical applications. While deep learning-based …

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 …

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

Convolutional neural networks (CNN) have been extensively utilized for image
segmentation tasks, with the U-Net architecture emerging as a classical model in medical …

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 …

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art
performance in recent years. Particularly, in deep learning, U-Net model has been …

Medical image segmentation is a critical and important step for developing computer-aided
system in clinical situations. It remains a complicated and challenging task due to the large …