A Dynamic Context Encoder Network for Liver Tumor Segmentation.

Journal: Current medical imaging
Published Date: Jan 1, 2024

Abstract

BACKGROUND: Accurate segmentation of liver tumor regions in medical images is of great significance for clinical diagnosis and the planning of surgical treatments. Recent advancements in machine learning have shown that convolutional neural networks are powerful in such image processing while largely reducing human labor. However, the variable shape, fuzzy boundary, and discontinuous tumor region of liver tumors in medical images bring great challenges to accurate segmentation. The feature extraction capability of a neural network can be improved by expanding its architecture, but it inevitably demands more computing resources in training and hyperparameter tuning.

Authors

  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • Jing Fang
    Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, 330063, China.
  • Tao Jiang
    Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
  • Chaochao Zhou
    Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, U.S.
  • Liren Shao
    Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, 330063, China.
  • Yusheng Song
    Interventional Radiology, The People's Hospital of Ganzhou, Ganzhou, Jiangxi, 341000, China.

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