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 …

For the past several years, semantic segmentation method based on deep learning,
especially Unet, have achieved tremendous success in medical image processing. The U …

Recently, a growing interest has been seen in deep learning-based semantic segmentation.
UNet, which is one of deep learning networks with an encoder-decoder architecture, is …

Semantic Segmentation has been widely used in a variety of clinical images, which greatly
assists medical diagnosis and other work. To address the challenge of reduced semantic …

Background and objective: Medical image segmentation plays an important role in clinic.
Recently, with the development of deep learning, many convolutional neural network (CNN) …

Owing to the various object types and scales, complicated backgrounds, and similar
appearance between tissues in medical images, it is difficult to extract some valuable …

The context-based convolutional neural network (CNN) is one of the most well-known CNNs
to improve the performance of semantic segmentation. It has achieved remarkable success …

Medical image segmentation plays an important role in many clinical medicines, such as
medical diagnosis and computer-assisted treatment. However, due to the large quality …

Background and objective Convolutional neural networks (CNNs) play an important role in
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …

The advanced development of deep learning methods has recently made significant
improvements in medical image segmentation. Encoder–decoder networks, such as U-Net …