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

• Published in: AMIA Summits on Translational Science proceedings Vol. 2021; pp. 663 - 671
• Main Authors: Zhang, Zicong, Powell, Kimerly, Yin, Changchang, Cao, Shilei, Gonzalez, Dani, Hannawi, Yousef, Zhang, Ping
• Format: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2021
• Subjects: Algorithms; Attention; Brain - diagnostic imaging; Humans; Magnetic Resonance Imaging; White Matter - diagnostic imaging
• ISSN: 2153-4063; 2153-4063

White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical con-sequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinical scans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverages only FLAIR images with a spatially-registered white matter (WM) brain atlas to yield competitive WMH segmentation performance. Specifically, we designed a dual-path segmentation model with two novel connecting mechanisms, namely multi-input attention module (MAM) and attention fusion module (AFM) to fuse the information from two paths for accurate results. Experiments on two publicly available datasets show the effectiveness of the proposed BAGAU-Net. With only FLAIR images and WM brain atlas, BAGAU-Net outperforms the state-of-the-art method with T1-weighted images, paving the way for effective development of WMH segmentation. Availability: https://github.com/Ericzhang1/BAGAU-Net.