Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning.

Journal: International journal of radiation oncology, biology, physics
PMID: 38797498

Abstract

PURPOSE: Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than to whole-heart metrics. Magnetic resonance (MR)-guided radiation therapy (MRgRT) enables substructure visualization during daily localization, allowing potential for enhanced cardiac sparing. We extend a publicly available state-of-the-art deep learning framework, "No New" U-Net, to incorporate self-distillation (nnU-Net.wSD) for substructure segmentation for MRgRT.

Authors

  • Nicholas Summerfield
    Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin; Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin.
  • Eric Morris
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA.
  • Soumyanil Banerjee
    Computer Science, Wayne State University, Detroit, Michigan, USA.
  • Qisheng He
    Department of Computer Science, Wayne State University, Detroit, Michigan.
  • Ahmed I Ghanem
    Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA.
  • Simeng Zhu
    Department of Radiation Oncology, Henry Ford Health Systems, Detroit, MI, United States of America.
  • Jiwei Zhao
    Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China E-mail: 1173434259@qq.com.
  • Ming Dong
    Department of Computer Science, Wayne State University.
  • Carri Glide-Hurst
    Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin; Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin. Electronic address: glidehurst@humonc.wisc.edu.

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