Registration-guided deep learning image segmentation for cone beam CT-based online adaptive radiotherapy.

Journal: Medical physics
Published Date: May 4, 2022

Abstract

PURPOSE: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as cone beam computed tomography (CBCT). Direct application of deep learning (DL)-based segmentation to CBCT images suffered from issues such as low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration-guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models.

Authors

  • Lin Ma
    Department of Radiation Oncology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States.
  • Weicheng Chi
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
  • Howard E Morgan
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Mu-Han Lin
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Mingli Chen
    Department of Gastroenterology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.
  • David Sher
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Dominic Moon
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Dat T Vo
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Vladimir Avkshtol
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Weiguo Lu
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
  • Xuejun Gu
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.

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