Brain tissue segmentation is of great value in diagnosing brain disorders. Three-dimensional (3D) and two-dimensional (2D) segmentation methods for brain Magnetic Resonance Imaging (MRI) suffer from high time complexity and low segmentation accuracy, respectively. To address these two issues, we propose a Context-assisted full Attention Network (CAN) for brain MRI segmentation by integrating 2D and 3D data of MRI. Different from the fully symmetric structure U-Net, the CAN takes the current 2D slice, its 3D contextual skull slices and 3D contextual brain slices as the input, which are further encoded by the DenseNet and decoded by our constructed full attention network. We have validated the effectiveness of the CAN on our collected dataset PWML and two public datasets dHCP2017 and MALC2012. Our code is available at https://github.com/nwuAI/CAN.
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