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White Matter Hyperintensities (WMH) are the most common manifestation ofcerebral small vessel disease (cSVD) on the brain MRI. Accurate WMHsegmentation algorithms are important to determine cSVD burden and its clinicalconsequences. Most of existing WMH segmentation algorithms require both fluidattenuated inversion recovery (FLAIR) images and T1-weighted images as inputs.However, T1-weighted images are typically not part of standard clinicalscanswhich are acquired for patients with acute stroke. In this paper, we propose anovel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIRimages with a spatially-registered white matter (WM) brain atlas to yieldcompetitive WMH segmentation performance. Specifically, we designed a dual-pathsegmentation model with two novel connecting mechanisms, namely multi-inputattention module (MAM) and attention fusion module (AFM) to fuse theinformation from two paths for accurate results. Experiments on two publiclyavailable datasets show the effectiveness of the proposed BAGAU-Net. With onlyFLAIR images and WM brain atlas, BAGAU-Net outperforms the state-of-the-artmethod with T1-weighted images, paving the way for effective development of WMHsegmentation. Availability:https://github.com/Ericzhang1/BAGAU-Net

Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation