In recent decades, 3D bioprinting has garnered significant research attention due to its
ability to manipulate biomaterials and cells to create complex structures precisely. However …

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 …

Accurate 3D medical imaging can effectively assist doctors in diagnosing diseases.
Currently, deep learning-based segmentation methods have yielded good results but face …

Hybrid architectures based on Convolutional Neural Networks (CNN) and Transformers
have been extensively employed in medical image segmentation. However, previous …

Automatic liver tumor segmentation is a paramount important application for liver tumor
diagnosis and treatment planning. However, it has become a highly challenging task due to …

High-performance segmentation can help physicians complete clinical diagnosis in a timely
manner, thereby determining critical treatment periods and improving the efficiency of stroke …

Segmentation of the liver from computed tomography images is an essential and critical task
in medical image analysis, with significant implications for liver disease diagnosis and …

In recent years, with the continuous improvement in the economic conditions of our people,
people pay more and more attention to the spiritual aspect of consumption. Therefore …

To address the problems of less semantic information and low measurement accuracy when
the SSD (single shot multibox detector) algorithm detects small targets, an MPH‐SSD …

Foreground segmentation algorithm aims to precisely separate moving objects from the
background in various environments. However, the interference from darkness, dynamic …