Oct 7, 2019 · When training Deep Neural Networks (DNN) to segment WMH, data pooling may be used to increase the training dataset size. However, it is not yet ...

White Matter Hyperintensities (WMH) are imaging biomarkers which indicate cerebral microangiopathy, a risk factor for stroke and vascular dementia.

Purpose To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm ...

The impact of sampling ratios between different datasets with varying data quality and lesion volumes is investigated and systematic changes in DNN ...

Feb 15, 2019 · In conclusion, the presented WMH segmentation pipeline was demonstrated on highly heterogeneous large-scale multi-site data. We applied it ...

Aug 15, 2021 · Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects.

Nov 23, 2023 · This also enabled post-hoc pooling of multicenter data of a heterogeneous population, improving generalizability to a memory clinic setting.

Jul 29, 2022 · In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH.

Lesions in different brain regions were characterized by their differential characteristics of signal strength, size/shape, heterogeneity, and texture.

In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions.