• 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:

Duan, Lijuan (Duan, Lijuan.) (学者:段立娟) | Feng, Xuan (Feng, Xuan.) | Chen, Jie (Chen, Jie.) | Xu, Fan (Xu, Fan.)

收录:

EI Scopus

摘要:

Nuclei instance segmentation is a critical part of digital pathology analysis for cancer diagnosis and treatments. Deep learning-based methods gradually replace threshold-based ones. However, automated techniques are still challenged by the morphological diversity of nuclei among organs. Meanwhile, the clustered state of nuclei affects the accuracy of instance segmentation in the form of over-segmentation or under-segmentation. To address these issues, we propose a novel network consists of a multi-scale encoder and a dual-path decoder. Features with different dimensions generated from the encoder are transferred to the decoder through skip connections. The decoder is separated into two subtasks to introduce boundary information. While an aggregation module of contour and nuclei is attached in each decoder for encouraging the model to learn the relationship between them. Furthermore, this avoids the splitting effect of independent training. Experiments on the 2018 MICCAI challenge of Multi-Organ Nuclei Segmentation dataset demonstrate that our proposed method achieves state-of-the-art performance. © 2020, Springer Nature Switzerland AG.

关键词:

Signal encoding Image segmentation Decoding Computer vision Deep learning

作者机构:

  • [ 1 ] [Duan, Lijuan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Duan, Lijuan]Beijing Key Laboratory of Trusted Computing, Beijing; 100124, China
  • [ 3 ] [Duan, Lijuan]National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing; 100124, China
  • [ 4 ] [Feng, Xuan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Feng, Xuan]Beijing Key Laboratory of Trusted Computing, Beijing; 100124, China
  • [ 6 ] [Feng, Xuan]National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing; 100124, China
  • [ 7 ] [Chen, Jie]Peng Cheng Laboratory, Shen Zhen; 518055, China
  • [ 8 ] [Xu, Fan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Xu, Fan]Beijing Key Laboratory of Trusted Computing, Beijing; 100124, China
  • [ 10 ] [Xu, Fan]National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing; 100124, China

通讯作者信息:

  • [chen, jie]peng cheng laboratory, shen zhen; 518055, china

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: 0302-9743

年份: 2020

卷: 12305 LNCS

页码: 341-352

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 1

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:401/4912067
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