Abstract #1023

StackGen-Net: A Stacked Generalization of 3D Orthogonal Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities

Lavanya Umapathy¹, Gloria J Guzman Perez-Carrillo², Mahesh Bharath Keerthivasan^2,3, Maria I Altbach², Blair Winegar², Craig Weinkauf⁴, and Ali Bilgin^1,2,5

¹Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, ²Department of Medical Imaging, University of Arizona, Tucson, AZ, United States, ³Siemens Healthcare USA, Tucson, AZ, United States, ⁴Department of Surgery, University of Arizona, Tucson, AZ, United States, ⁵Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States

Detection and quantification of White Matter Hyperintensities (WMH) on T2-FLAIR images can provide valuable information to assess neurological disease progression. We propose a fully automated stacked generalization ensemble of three orthogonal 3D Convolutional Neural Networks (CNNs), StackGen-Net, to detect WMH on 3D FLAIR images. Each orthogonal CNN predicts WMH on axial, sagittal, and coronal orientations. The posteriors are then combined using a Meta CNN. StackGen-Net outperforms individual CNNs in the ensemble, their ensemble combination, as well as some state-of-the-art deep learning-based models. StackGen-Net can reliably detect and quantify WMH in clinically feasible times, with performance comparable to human inter-observer variability.

This abstract and the presentation materials are available to members only; a login is required.

Join Here