3D-MRI brain glioma intelligent segmentation based on improved 3D U-net network.

Journal: PloS one
Published Date: Jun 13, 2025

Abstract

PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection.

Authors

  • Tingting Wang
    Department of Anesthesiology, Taizhou Hospital, Linhai, China.
  • Tong Wu
    National Clinical Research Center for Obstetrical and Gynecological Diseases Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan China.
  • Defu Yang
    School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China; Department of Psychiatry, University of North Carolina at Chapel Hill, USA. Electronic address: dfyang@hdu.edu.cn.
  • Ying Xu
    School of Biological and Food Engineering Changzhou University Changzhou Jiangsu China.
  • Dongyang Lv
    Department of Radiationtherapy, General Hospital of Northern Theater Command, Shenyang, China.
  • Tong Jiang
    Key Laboratory of Chinese Internal Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China.
  • Hengjiao Wang
    Department of Radiationtherapy, General Hospital of Northern Theater Command, Shenyang, China.
  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Shengnan Xu
    Department of Radiationtherapy, General Hospital of Northern Theater Command, Shenyang, China.
  • Ying Yan
    School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, Guizhou, China.
  • Baoguang Lin
    Department of Radiationtherapy, General Hospital of Northern Theater Command, Shenyang, China.

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