Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 20, 2022

Abstract

BACKGROUND AND OBJECTIVE: Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns and growing locations. Learning semantic and spatial relations between different feature channels, image regions and positions is critical yet challenging.

Authors

  • Ping Xuan
    School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
  • Bin Jiang
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Hui Cui
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, PR China.
  • Qiangguo Jin
    School of Software, Northwestern Polytechnical University, Xi' an, China.
  • Peng Cheng
    University of Kansas Medical Center, Department of Internal Medicine, Division of Medical Informatics, Kansas City, KS, USA.
  • Toshiya Nakaguchi
  • Tiangang Zhang
    School of Mathematical Science, Heilongjiang University, Harbin 150080, China. zhang@hlju.edu.cn.
  • Changyang Li
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia. Electronic address: changyang.li@sydney.edu.au.
  • Zhiyu Ning
    Sydney Polytechnic Institute, Sydney, Australia.
  • Menghan Guo
    GMH Technology Pty Ltd, Sydney, Australia.
  • Linlin Wang
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.

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