Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages.

Journal: Magnetic resonance imaging
PMID: 37541456

Abstract

INTRODUCTION: Minimally-invasive surgical techniques for intracerebral hemorrhage (ICH) evacuation use imaging to guide the suction, lysing and/or drainage from the hemorrhage site via various designs. A previous international surgical study has shown that reduction of hematoma volume below 15 ml is indicative of improved long term patient outcomes. The study noted a need for tools to periodically visualize remaining clot during intervention to increase the likelihood of evacuating sufficient clot volumes without endangering rebleeds. Robust segmentation of MRI could guide surgeons and radiologists regarding remaining regions and approaches for prudent evacuation. We thus propose a Convolutional Neural Network (CNN) to identify and autonomously segment clot and peripheral edema in MR images of the brain and generate an estimate of the remaining clot volume.

Authors

  • Thomas Lilieholm
    Department of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA. Electronic address: tlilieholm@wisc.edu.
  • Alan McMillan
    Department of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA; Deparment of Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, USA.
  • Azam Ahmed
    Department of Neurosurgery, University of Wisconsin at Madison, Madison, WI, USA.
  • Matthew Henningsen
    Department of Electrical Engineering, University of Wisconsin at Madison, Madison, WI, USA.
  • Matthew Larson
    Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA.
  • Walter F Block
    Department of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA; Deparment of Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, USA.

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