Abstract #2835

Deep Learning with Synthetic Diffusion-Weighted Images for Acute Ischemic Stroke Detection

CHRISTIAN FEDERAU^1,2, Javier A. Montoya-Zegarra¹, Soren Christensen³, Julian Maclaren³, Johanna Ospel², Victor Schulze-Zachau², Maarten Lansberg³, and Sebastian Kozerke¹

¹Institute for Biomedical Engineering, University and ETH Zurich, Zürich, Switzerland, ²Department of Radiology, University Hospital Basel, Basel, Switzerland, ³Neurology, Stanford University, Palo Alto, CA, United States

We studied the feasibility and accuracy of a deep learning algorithm trained on one million realistic synthetic acute stroke lesion images to detect and segment stroke lesions on clinical MR DW images. We compared this method to a more conventional approach, where a deep learning algorithm was trained on 10’000 human labelled images.

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