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Abstract #4136

Automated Segmentation of Salvageable Ischemic Brain Tissue using Convolutional Neural Networks with DWI and FLAIR MRI

Ryan Andrew Rava1,2, Kenneth V. Snyder2,3, Muhammad Waqas2,3, Elad I. Levy2,3, Jason M. Davies2,3, Adnan H. Siddiqui2,3, and Ciprian N. Ionita1,2,3
1Biomedical Engineering, University at Buffalo, Buffalo, NY, United States, 2Canon Stroke and Vascular Research Center, Buffalo, NY, United States, 3Neurosurgery, University at Buffalo, Buffalo, NY, United States

Convolutional neural networks have the potential to predict penumbra volumes within acute ischemic stroke patients to determine their eligibility for mechanical thrombectomy based on the Defuse 3 clinical trial. Currently, computed tomography perfusion is the main method used to quantify penumbra volumes but not all stroke centers have this modality available. In this study, 2 networks were developed to automatically segment penumbra using FLAIR and DWI and performance metrics comparing each network’s predictions with ground truth penumbra (dual network: Dice=0.61, sensitivity=0.68, PPV=0.59, multi-input network: Dice=0.61, sensitivity=0.62, PPV=0.64) indicate a multi-input network is the most capable of segmenting penumbra tissue.

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