Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.

Journal: Cancer medicine
Published Date:

Abstract

OBJECTIVE: To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis.

Authors

  • Runyuan Wang
    Department of Digital Medicine, College of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.
  • Xingcai Chen
    Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, 400038, P. R. China.
  • Xiaoqin Zhang
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Ping He
    Shanghai Hospital Development Center, Shanghai 200040, China. Electronic address: heping@shdc.org.cn.
  • Jinfeng Ma
    Department of General Surgery, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
  • Huilin Cui
    Department of Histology and Embryology, Shanxi Medical University, Taiyuan, China.
  • Ximei Cao
    Department of Histology and Embryology, Shanxi Medical University, Taiyuan, China.
  • Yongjian Nian
    Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China. Electronic address: yjnian@tmmu.edu.cn.
  • Ximing Xu
    Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang, People's Republic of China.
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Yi Wu
    School of International Communication and Arts, Hainan University, Haikou, China.