• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Zhang, Li (Zhang, Li.) | Zhu, BinBing (Zhu, BinBing.) | Liu, XiaoJian (Liu, XiaoJian.) | Ma, ChunPeng (Ma, ChunPeng.)

收录:

EI Scopus

摘要:

The semantic segmentation task of medical image is to segment the focus, organ or substructure of human body in medical image. It plays an important role in locating and identifying the diseased area and making medical plan. In various medical image segmentation tasks, the U-shaped architecture has achieved great success. Transunet introduces Transformer with global attention mechanism into the U-shaped architecture, which overcomes the inherent limitations of convolution, but because it still continues the original skip connections structure, it will bring the strong noise from features in the shallow network into the high semantic features of the deep network, thus affecting the segmentation accuracy. UTSN-net model based on the combination of Transformer and nonlocal attention mechanism is proposed. UTSN-net uses Transformer to overcome the inherent limitations of convolution, and introduces the skip connections module based on nonlocal attention mechanism into the U-shaped network, which can comprehensively consider the deep features with global context information and the shallow features with accurate high-resolution positioning information to improve the accuracy of segmentation results. Experiments on synapse multi-organ abdominal CT dataset verify that UTSN-net has better semantic segmentation performance. © 2023 SPIE.

关键词:

Computerized tomography Convolution Semantics Deep learning Network architecture Medical image processing Semantic Segmentation

作者机构:

  • [ 1 ] [Zhang, Li]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhu, BinBing]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Liu, XiaoJian]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 4 ] [Ma, ChunPeng]Faculty of Information Technology, Beijing University of Technology, Beijing, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

ISSN: 0277-786X

年份: 2023

卷: 12715

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

归属院系:

在线人数/总访问数:547/4931124
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司