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Abstract #4142

Tool for Image-guided Intracerebral Hemorrhage Evacuation: Automatic Segmentation of Clot, Edema, and Normal Brain Tissue

Thomas Lilieholm1, Alan McMillan1,2, Fang Liu1, Robert Moskwa1, Azam Ahmed3, and Walter F Block1,2,4
1Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 2Radiology, University of Wisconsin - Madison, Madison, WI, United States, 3Neurological Surgery, University of Wisconsin - Madison, Madison, WI, United States, 4Biomedical Engineering, University of Wisconsin - Madison, Madison, WI, United States

Improved image guidance is needed for neurosurgeons to reduce the residual remaining clot levels during minimally invasive evacuation of intracerebral hemorrhage (ICH) while not causing rebleeds. Neurosurgeons would benefit from a means to periodically render the clot volume against surrounding normal tissue during mechanical evacuation or pharmaceutical-based clot-busting. Using convolutional neural networks (CNN), we created machine learning models to automatically segment the constituent clot and edema induced by ICH cases using T2-weighted MR images. The CCN’s output results were found to be in agreement with manual segmentations of the same ICH cases.

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