Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors.

Journal: European journal of radiology
PMID: 40014944

Abstract

BACKGROUND: Automatic segmentation of thymic lesions in preoperative computed tomography (CT) images is crucial for accurate diagnosis but remains time-consuming. Although UNet is widely used in medical imaging, its performance is limited by the inherent drawbacks of convolutional neural networks (CNNs), such as restricted receptive fields and limited global context modeling, which affect segmentation efficiency.

Authors

  • Qiongliang Liu
    Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Taiyu Qu
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Xiangnan Xu
    School of Mathematics and Statistics, The University of Sydney, Sydeny, New South Wales 2006, Australia.
  • Xiaolong Li
    Auckland Tongji Medical & Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China.
  • Dong Lin
    Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Hua Liu
    Department of Ophthalmology and Visual Sciences, University of Texas Medical Branch, TX, 77555-0144, USA.
  • Qinghao Liu
  • Kang Qi
    Department of Thoracic Surgery, Peking University First Hospital, Beijing, China.
  • Jiang Fan
    Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Lei Zhou
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xing Wang
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.

Keywords

External Resources

Popular Topics
Recent Journals