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Abstract #4809

Volumetric Segmentation of Acute Brain Infarcts on Diffusion-Weighted Imaging using Deep Learning

Ken Chang1, James Brown1, Andrew L Beers1, Katharina Hoebel1, Jay Patel1, Otto Rapalino2, Bruce Rosen1, Hakan Ay1, and Jayashree Kalpathy-Cramer2

1Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Massachusetts General Hospital, Boston, MA, United States

Rapid and accurate evaluation of stroke is imperative as currently available treatments are constrained by a narrow time window. Diffusion Weighted Magnetic Resonance (DWI) is a key imaging modality in stroke evaluation as it allows for assessment of the extent of acute ischemic brain injury. Nonetheless, manual delineation of stroke regions is expensive, time-consuming, and subject to inter-rater variability. In this study, we sought to develop a deep learning approach for ischemic stroke volumetric segmentation in a large clinical dataset of 1,205 patients from the NIH-funded Heart-Brain Interactions in Human Acute Ischemic Stroke Study utilizing only DWI imaging.

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