HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 14, 2024

Abstract

BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular form of the esophagus and small size, the inconsistency of spatio-temporal structure, and low contrast of esophagus and its peripheral tissues in medical images. The objective of this study is to improve the segmentation effect of esophageal squamous cell carcinoma lesions.

Authors

  • Muwei Jian
    School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China.
  • Chen Tao
    Gansu Key Laboratory of Biomonitoring and Bioremediation for Environmental Pollution, School of Life Sciences, Lanzhou University, Lanzhou 730000, China.
  • Ronghua Wu
    School of Information Science and Technology, Linyi University, Linyi, China.
  • Haoran Zhang
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Xiaoguang Li
    Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Yanlei Wang
    Department of General Surgery, Third Xiangya Hospital, Central South University, 138 Tongzipo Street, Changsha, 410013, Hunan, China.
  • Lizhi Peng
    Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan, China.
  • Jian Zhu

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