Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.

Journal: Tomography (Ann Arbor, Mich.)
PMID: 40137561

Abstract

BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor growth and characterizing the tumors as well.

Authors

  • Satvik Nayak
    Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USA.
  • Henry Salkever
    Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USA.
  • Ernesto Diaz
    Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USA.
  • Avantika Sinha
    Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USA.
  • Nikhil Deveshwar
  • Madeline Hess
    Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging, University of California, San Francisco, San Francisco, California.
  • Matthew Gibbons
    Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USA.
  • Sule Sahin
    Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USA.
  • Abhejit Rajagopal
  • Peder E Z Larson
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
  • Renuka Sriram
    Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USA.

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