Abdomen CT multi-organ segmentation using token-based MLP-Mixer.

Journal: Medical physics
Published Date: Dec 20, 2022

Abstract

BACKGROUND: Manual contouring is very labor-intensive, time-consuming, and subject to intra- and inter-observer variability. An automated deep learning approach to fast and accurate contouring and segmentation is desirable during radiotherapy treatment planning.

Authors

  • Shaoyan Pan
    Department of Biomedical Informatics, Emory University, Atlanta, GA 30308, United States of America.
  • Chih-Wei Chang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
  • Tonghe Wang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Jacob Wynne
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
  • Mingzhe Hu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
  • Yang Lei
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Tian Liu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Pretesh Patel
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina.
  • Justin Roper
    Radiology Oncology, Emory University, 1365 Clifton Road, Department of Radiation Oncology, Atlanta, Atlanta, Georgia, 30322, UNITED STATES.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.

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