Attention Blocks Improve White Matter Hyperintensity Semantic Segmentation using U-Nets

White matter hyperintensities (WMHs) are a common finding on magnetic resonance (MR) images in older individuals, appearing as high-signal intensity regions on fluid-attenuated inversion recovery (FLAIR) imaging. People with high WMH volume are at increased risk for dementia and stroke, controlling for vascular risk factors, but WMH burden is not reliably assessed in clinical practice. Manual segmentation of WMHs is accepted as the gold standard (or ground truth), however, it is a laborious and time-consuming method. Newer machine learning (ML)-based approaches are being proposed as alternatives to manual segmentation. Among these approaches, U-Net convolutional neural networks have demonstrated good WMH segmentation performance. However, even state-of-the-art ML models sometimes fail to correctly identify WMHs and their boundaries with sufficient accuracy. Attention blocks have emerged as a potential solution for improving the performance of U-Net models by enhancing the ability of the model to focus on relevant features in the data. We investigated the effectiveness of attention blocks in U-Net models for WMH segmentation compared to three other models (U-Net++, U-Net3+, and a standard U-Net). Attention blocks significantly improved the F-measure score for WMH segmentation (0.811 vs 0.789 for next best model, p=0.04) in a diverse brain imaging dataset. This study demonstrates that attention blocks enhance U-Net models used for WMH identification and classification.