融合多尺度门控卷积和窗口注意力的结肠息肉分割

Colon Polyp Segmentation Fusing Multi-scale Gate Convolution and Window Attention

结肠息肉的准确分割对于切除异常组织和降低息肉转换为结肠癌的风险具有重要意义.目前的结肠息肉分割模型在对息肉图像进行分割时存在着较高的误判率和分割精度较低的问题.为了实现对息肉图像的精准分割,提出了一种融合多尺度门控卷积和窗口注意力的结肠息肉分割模型(MGW-Net).首先,设计一种改进的多尺度门控卷积块(MGCM)取代U-Net的卷积块,来实现对结肠息肉图像信息的充分提取.其次,为了减少跳跃连接处的信息损失并充分利用网络底部信息,结合改进的空洞卷积和混合增强的残差窗口注意力构建了多信息融合增强模块(MFEM),以优化跳跃连接处的特征融合.在CVC-ClinicDB和Kvasir-SEG数据集上的实验结果表明,MGW-Net的相似性系数分别为93.8%和92.7%,平均交并比分别为89.4%和87.9%,在CVC-ColonDB、CVC-300和ETIS数据集上的实验结果表明其拥有较强的泛化性能,从而验证了MGW-Net可以有效地提高对结肠息肉分割的准确性和鲁棒性.

Accurate segmentation of colon polyps is important to remove abnormal tissue and reduce the risk of polyps converting to colon cancer.The current colon polyp segmentation model has the problems of high misjudgment rate and low segmentation accuracy in the segmentation of polyp images.To achieve accurate segmentation of polyp images,this study proposes a colon polyp segmentation model(MGW-Net) combining multi-scale gated convolution and window attention.Firstly,it designs an improved multi-scale gate convolution module(MGCM) to replace the U-Net convolutional block to achieve full extraction of colon polyp image information.Secondly,to reduce the information loss at the skip connection and make full use of the information at the bottom of the network,the study builds a multiinformation fusion enhancement module(MFEM) by combining improved dilated convolution and hybrid enhanced residual window attention to optimize the feature fusion at the skip connection.Experimental results on CVC-ClinicDB and Kvasir-SEG data sets show that the similarity coefficients of MGW-Net are 93.8% and 92.7%,and the average crossover ratio is 89.4% and 87.9%,respectively.Experimental results on CVC-ColonDB,CVC-300,and ETIS datasets show that MGW-Net has strong generalization performance,which verifies that MGW-Net can effectively improve the accuracy and robustness of colon polyp segmentation.