融合视觉Transformer和轴向注意的结直肠息肉分割

Colorectal Polyp Segmentation Combining Pyramid Vision Transformer and Axial Attention

针对结直肠息肉自动准确分割存在的挑战,提出了一种结直肠分割网络PVTA-Net。该网络由PVTv2、特征金字塔网络(FPN)、空间金字塔(ASPP)、多头自注意机制(MHSA)以及并行轴向注意模块(PAA-d)组成:通过PVTv2提取不同尺度的特征图;利用FPN不同层次特征进行融合,得到增强的特征图;利用ASPP聚合由FPN得到的特征图;通过MHSA获得包含所有输入图像的感受野;利用PAA-d生成具有全局关系的特征。采用ColonDB等5个数据集对PVTA-Net和主流息肉分割网络进行对比测试,结果表明,PVTA-Net优于现有主流基线网络。为了验证PVTA-Net的泛化性能,将其用于COVID-19肺部CT图像分割,结果表明,PVTA-Net优于主流基线网络。

To address the challenges of automatic and accurate segmentation of colorectal polyps,a colorectal segmentation network is proposed:PVTA-Net.The network consists of PVTv2,feature pyramid network(FPN),spatial pyramid(ASPP),multi-headed self-attentive mechanism(MHSA),and parallel axial attention module(PAA-d):extracting feature maps at different scales by PVTv2;using FPN different levels of features are fused to obtain the enhanced feature maps;ASPP is used to aggregate the feature maps obtained by FPN;MHSA is used to obtain the perceptual field containing all input images;and PAA-d is used to generate features with global relationships.Five datasets,including ColonDB,are used to test the comparison between PVTA-Net and mainstream polyp segmentation networks,and the results show that PVTA-Net outperforms the existing mainstream baseline networks.To verify the generalization performance of PVTA-Net,it is used for COVID-19 lung CT image segmentation,and the results show that,PVTA-Net outperforms the mainstream baseline network.