Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection.

Journal: Journal of neuroradiology = Journal de neuroradiologie
PMID: 39521273

Abstract

PURPOSE: Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contrast CT scans of the head (NCCTH) is challenging given interference from the adjacent calvarium. This work explores the utility of a NCCTH bone removal algorithm for improving SDH detection.

Authors

  • Masis Isikbay
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave, M-396, San Francisco, CA, 94143, USA. masis.isikbay@ucsf.edu.
  • M Travis Caton
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (W.F.W., M.T.C., K.M., S.A.G., E.G., M.H.R., G.C.G., K.P.A.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (W.F.W., M.T.C., K.M., K.P.A.).
  • Jared Narvid
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
  • Jason Talbott
    Department of Radiology and Biomedical Imaging, Zuckerberg San Francisco General Hospital, University of California, San Francisco, CA, USA.
  • Soonmee Cha
    Department of Radiology and Biomedical Imaging, University of California At San Francisco, 350 Parnassus Ave, Suite 307H, San Francisco, CA, 94143-0628, USA.
  • Evan Calabrese
    Department of Radiology and Biomedical Imaging, University of California At San Francisco, 350 Parnassus Ave, Suite 307H, San Francisco, CA, 94143-0628, USA. evan.calabrese@ucsf.edu.

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