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ORIGINAL RESEARCH ADULT BRAIN A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images L. Umapathy, G.G. Perez-Carrillo, M.B. Keerthivasan, J.A. Rosado-Toro, M.I. Altbach, B. Winegar, C. Weinkauf, and A. Bilgin, for the Alzheimer’s Disease Neuroimaging Initiative ABSTRACT BACKGROUND AND PURPOSE: Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images. MATERIALS AND METHODS: Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n ¼ 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically eval- uated on a test cohort (n ¼ 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort
American Journal of Neuroradiology – American Journal of Neuroradiology
Published: Apr 1, 2021
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