In this study, variations of 2D, 2.5D and 3DU-shaped convolutional neural networks (U-Net CNNs) were used to perform semantic segmentation on FLAIR images.

Abstract—White-matter hyperintensity (WMH) is associated with many disorders where it is suggestive of underlying cere- brovascular, small-vessel disease ...

Our WMH segmentation approach consisted of two steps: (1) Data Preparation: Preprocessing the FLAIR images, and (2) CNN U-Net: Implementing slice-based (2D), ...

Variations of 2D, 2.5D and 3DU-shaped convolutional neural networks (U-Net CNNs) were used to perform semantic segmentation on FLAIR images and it was ...

Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects.

Introduction: Vascular damage in Alzheimer's disease (AD) has shown conflicting findings particularly when analyzing longitudinal data. We introduce white ...

Missing: CNNs. | Show results with:CNNs.

Feb 1, 2024 · We explored convolutional neural networks (CNNs) for performing semantic segmentation of WMHs in FLAIR images. Two sets of experiments were ...

Two sets of experiments were conducted: (1) Variations of U-shaped CNNs (U-Nets) were evaluated in 186 individuals, specifically, four architectures (VGG16, ...

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white ...