Abstract #3260

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

Thomas Lilieholm¹, Matt Henningsen², Azam Ahmed³, Alan McMillan^1,4, and Walter F Block^1,4,5

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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