  White Matter Hyperintensity (WMH) is an imaging feature related to various
diseases such as dementia and stroke. Accurately segmenting WMH using computer
technology is crucial for early disease diagnosis. However, this task remains
challenging due to the small lesions with low contrast and high discontinuity
in the images, which contain limited contextual and spatial information. To
address this challenge, we propose a deep learning model called 3D Spatial
Attention U-Net (3D SA-UNet) for automatic WMH segmentation using only Fluid
Attenuation Inversion Recovery (FLAIR) scans. The 3D SA-UNet introduces a 3D
Spatial Attention Module that highlights important lesion features, such as
WMH, while suppressing unimportant regions. Additionally, to capture features
at different scales, we extend the Atrous Spatial Pyramid Pooling (ASPP) module
to a 3D version, enhancing the segmentation performance of the network. We
evaluate our method on publicly available dataset and demonstrate the
effectiveness of 3D spatial attention module and 3D ASPP in WMH segmentation.
Through experimental results, it has been demonstrated that our proposed 3D
SA-UNet model achieves higher accuracy compared to other state-of-the-art 3D
convolutional neural networks.
