MRI Brain Tumor Segmentation Network Using Multi-scale Non-local Self-attention
To address issues of the limited receptive field and insufficient global information of the U-Net model in MRI brain tumor segmentation,this study proposes an improved U-Net model,i.e.,PyCSAU-Net,by introducing non-local self-attention mechanism and multi-scale pyramidal convolution.The given model leverages the three-dimensional U-Net as the baseline and introduces the extended three-dimensional non-local attention to the horizontal connection of the fourth layer,which solves the issue of insufficient long-term modeling ability caused by the limited convolution kernel size to a certain extent,thus improving the segmentation performance.Moreover,it replaces the normal convolution by three-dimensional pyramidal convolution with multi-scale characteristics to capture more discriminant deep features of brain tumors at multi-levels and multi-resolutions.The segmentation results of 0.904/0.901,0.781/0.774,and 0.825/0.824 are achieved on the publicly BraTS 2019 and BraTS 2020 validation datasets on the whole tumor,enhanced tumor,and tumor core,respectively.It demonstrates the effectiveness and competitiveness of PyCSAU-Net for the brain tumor segmentation task.
万方数据
维普
