Convolutional Neural Networks For Automated Edema Segmentation in Patients With Intracerebral HemorrhageDownload PDF

11 Apr 2018 (modified: 05 May 2023)MIDL 2018 Abstract SubmissionReaders: Everyone
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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