U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

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 …

Automatic assessing the location and extent of liver and liver tumor is critical for radiologists,
diagnosis and the clinical process. In recent years, a large number of variants of U-Net …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the
world and caused significant impact on the public health and economy. However, there is …

Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist
radiologists in reading images, but also provide image-guided clinical diagnosis and …

Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic
liver and tumor segmentation are of great value in clinical practice as they can reduce …

Owing to the various object scales and high similarity with the surrounding organs (eg,
kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the …

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 …

In recent years, semantic segmentation method based on deep learning provides advanced
performance in medical image segmentation. As one of the typical segmentation networks …