多尺度非局部自注意力MRI脑肿瘤分割网络

MRI Brain Tumor Segmentation Network Using Multi-scale Non-local Self-attention

针对U-Net模型在MRI脑肿瘤分割上存在的感受野受限和全局信息捕获不足问题,通过引入非局部自注意力机制与多尺度的金字塔卷积提出一种改进U-Net模型—PyCSAU-Net.该模型以三维U-Net作为基础网络,在第4层横向连接位置引入扩展的三维非局部注意力模块,通过改善网络因卷积核大小受限导致的长距离建模能力不足问题来提升脑肿瘤分割精度;此外,在网络下采样阶段将普通卷积替换为具有多尺度特点的三维金字塔卷积,在多级别和分辨率下来提取更具判别性的脑肿瘤深度特征.在公开的BraTS 2019和BraTS 2020验证集上在完全肿瘤、增强肿瘤和肿瘤核心分割上分别取得了0.904/0.901、0.781/0.774和0.825/0.824的分割精度,表明所提出PyCSAU-Net方法在脑肿瘤分割任务上的有效性和竞争力.

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.