  Objective: Magnetic resonance imaging (MRI) has been widely used for the
analysis and diagnosis of brain diseases. Accurate and automatic brain tumor
segmentation is of paramount importance for radiation treatment. However, low
tissue contrast in tumor regions makes it a challenging task.Approach: We
propose a novel segmentation network named Category Guided Attention U-Net (CGA
U-Net). In this model, we design a Supervised Attention Module (SAM) based on
the attention mechanism, which can capture more accurate and stable long-range
dependency in feature maps without introducing much computational cost.
Moreover, we propose an intra-class update approach to reconstruct feature maps
by aggregating pixels of the same category. Main results: Experimental results
on the BraTS 2019 datasets show that the proposed method outperformers the
state-of-the-art algorithms in both segmentation performance and computational
complexity. Significance: The CGA U-Net can effectively capture the global
semantic information in the MRI image by using the SAM module, while
significantly reducing the computational cost. Code is available at
https://github.com/delugewalker/CGA-U-Net.
