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Abstract #4879

Detection of White Matter Hyperintensities using Ensemble 3D Deep Learning Networks

Lavanya Umapathy1, Gloria Guzman2, Jose Rosado-Toro2, Gokhan Kuyumcu2, Maria Altbach2, Blair Winegar2, Craig Weinkauf3, and Ali Bilgin1,4

1Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2Department of Medical Imaging, University of Arizona, Tucson, AZ, United States, 3Department of Surgery, University of Arizona, Tucson, AZ, United States, 4Biomedical Engineering, University of Arizona, Tucson, AZ, United States

White matter hyperintensities (WMH), hyperintense on T2-weighted FLAIR images are prominent features of demyelination and axonal degeneration in cerebral white matter. The time-consuming nature of manual segmentation necessitates the need for faster and reliable automated segmentation algorithms. In this work, we propose three deep learning architectures for WMH detection on 3D FLAIR images: a modified UNET3D, Res-UNET3D and their ensemble combination. Two UNET3D and two Res-UNET3D were trained with random initialization using 3D patches sampled from within the brain. The posterior probabilities for WMH from individual networks were averaged to obtain a revised posterior probability for the ensemble. Performance of the individual networks as well as that of the ensemble was assessed using dice and precision scores.

It was observed that the ensemble of 3D networks yields improved dice and precision scores in comparison to an average of individual networks, thereby reducing the effect of choice of network or parameters. Furthermore, the average dice scores for the ensemble approached the inter-observer variability of human observers.

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