Convolutional Neural Networks For Automated Edema Segmentation in Patients With Intracerebral Hemorrhage
Abstract: Intracerebral hemorrhage (ICH) is a common type of stroke with high morbidity and mortality
rate. Edema often forms around ICH. Because edema increases the chance of poor outcome,
edema quantification is needed for finding the optimal ICH treatment. CNN has been proven
to be a reliable method in medical image segmentation. In this study, we introduce CNN to
develop an automated method for edema and ICH quantification. We found that our CNN is a promising quantification method for edema.
Author Affiliation: Academic Medical Center
Keywords: Brain edema, convolutional neural networks, intracerebral hemorrhage
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