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Abstract #3260

Neural Network for Autonomous Segmentation and Volumetric Assessment of Clot and Edema in Intracerebral Hemorrhages

Thomas Lilieholm1, Matt Henningsen2, Azam Ahmed3, Alan McMillan1,4, and Walter F Block1,4,5
1Medical Physics, University of Wisconsin at Madison, Madison, WI, United States, 2Electrical Engineering, University of Wisconsin at Madison, Madison, WI, United States, 3Neurological Surgery, University of Wisconsin at Madison, Madison, WI, United States, 4Radiology, University of Wisconsin at Madison, Madison, WI, United States, 5Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, United States

Previous work has shown that minimally-invasive reduction of hematoma volume in intracerebral hemorrhage to a threshold of 15mL is indicative of improved long term patient outcome. To attain this goal, image-guided minimally-invasive surgical techniques are applied to both lyse clot material and drain from the site of hemorrhage via a porous catheter. We propose a Convolutional Neural Network to identify and autonomously segment clot and peripheral edema in MR images of the brain for volumetric analysis, and image-guidance during evacuation. Quantitative measurements produced in this way can be used for superior clot visualization and direct measurement of remaining clot volume.

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