[Not Available].

Journal: Medical physics
Published Date: Feb 6, 2024

Abstract

BACKGROUND:: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and promptable auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning.

Authors

  • Lian Zhang
    Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA.
  • Zhengliang Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Lu Zhang
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Zihao Wu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Xiaowei Yu
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Jason Holmes
    Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ (Y.H., J.H., S.H.P., N.Y.Y., W.L.); Cornell University, Ithaca, NY (Y.H.); Department of Electric Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA (Y.H.); and Department of Radiation Oncology, Mayo Clinic, Rochester, MN (A.B., E.L.M., D.K.E., D.M.R., S.S., C.L.H., B.E.B., M.W.).
  • Hongying Feng
    Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA.
  • Haixing Dai
    School of Computing, University of Georgia, Athens, GA, United States.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • William W Wong
    Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
  • Sujay A Vora
    Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA.
  • Dajiang Zhu
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Tianming Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.

Keywords

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