Abstract
Diffusion magnetic resonance imaging (dMRI) is a non-invasive technique for studying the microstructure properties of brain white matter (WM) in vivo. Segmentation of WM fiber tracts can be used to characterize the topological structure of the human brain and to exploit the biological landmark of abnormal areas by dMRI. To improve the performance of the fiber tract segmentation, we propose a novel U-Net based architecture with dense criss-cross attention, which captures non-local rich global contextual information more efficiently. Our model is evaluated using the real brain data from Human Connectome Project (HCP). Extensive experiments demonstrate that our model improves the performance of fiber tract segmentation, especially for the fiber bundle with complicated topology structure.
This work was supported in part by the Natural Science Foundation of Heilongjiang Province under Grant LH2021F046.
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Yin, H., Xu, P., Cui, H., Chen, G., Ma, J. (2022). DC\(^2\)U-Net: Tract Segmentation in Brain White Matter Using Dense Criss-Cross U-Net. In: Cetin-Karayumak, S., et al. Computational Diffusion MRI. CDMRI 2022. Lecture Notes in Computer Science, vol 13722. Springer, Cham. https://doi.org/10.1007/978-3-031-21206-2_10
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