Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet.

Journal: Computer methods and programs in biomedicine
Published Date: Feb 3, 2023

Abstract

BACKGROUND AND OBJECTIVE: The artificial segmentation of early gastric cancer (EGC) lesions in gastroscopic images remains a challenging task due to reasons including the diversity of mucosal features, irregular edges of EGC lesions and nuances between EGC lesions and healthy background mucosa. Hence, this study proposed an automatic segmentation framework: co-spatial attention and channel attention based triple-branch ResUnet (CSA-CA-TB-ResUnet) to achieve accurate segmentation of EGC lesions for aiding clinical diagnosis and treatment.

Authors

  • Wenju Du
    Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
  • Nini Rao
    Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China. raonn@uestc.edu.cn.
  • Jiahao Yong
    Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
  • Prince Ebenezer Adjei
    Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Xiaoming Hu
    Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Xiaotong Wang
    Clinical Medical College of Acupuncture, Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine.
  • Tao Gan
    Department of Gastroenterology, West China Hospital, Chengdu, Sichuan 610041, China.
  • Linlin Zhu
    Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, 610017, China.
  • Bing Zeng
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China.
  • Mengyuan Liu
  • Yongxue Xu
    Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

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