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Abstract #0902

Improved Prediction of the Final Infarct from Acute Stroke Neuroimaging Using Deep Learning

Yilin Niu1, Enhao Gong2, Junshen Xu1, Thoralf Thamm2, John Pauly2, and Greg Zaharchuk2

1Tsinghua University, Beijing, China, 2Stanford University, Stanford, CA, United States

Magnetic Resonance Imaging (MRI) is a widely-used technique for clinics. Its advantages in providing multiple complimentary contrasts make it the best image tool for detecting presenting lesions in the brain. A lot methods have been proposed for lesion detection and segmentations using machine learning techniques. It is more sophisticated than common computer vision tasks since the estimation of treatment outcomes are not merely determined by lesions captured by current MR images. We targeted to develop an algorithm, based on 3D Convolutional Neural Network, to predict the final lesion shown on day-90 scans by processing the day-0 acute stroke images.

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