HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date: May 21, 2024

Abstract

BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity.

Authors

  • Ning Yuan
    Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
  • Yongtao Zhang
    Department of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu 476000, China.
  • Kuan Lv
    Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
  • Yiyao Liu
    School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China.
  • Aocai Yang
    Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China.
  • Pianpian Hu
    Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China.
  • Hongwei Yu
    Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China.
  • Xiaowei Han
    Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
  • Xing Guo
    Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
  • Junfeng Li
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China.
  • Tianfu Wang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Baiying Lei
  • Guolin Ma
    Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China. maguolin1007@qq.com.

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