融合Transformer和卷积的结直肠息肉分割算法

Colorectal polyp segmentation algorithm integrating Transformer and convolution

结直肠息肉大小不一、形态各异,特别是边界模糊导致难以准确定位,小尺寸息肉容易漏检,使得息肉分割存在较大的挑战。针对以上问题,提出一种融合Transformer和卷积的息肉分割算法。首先使用Transformer提取图像全局特征,保证网络全局建模的能力,提高息肉主体区域和模糊边界的定位能力。然后,引入卷积加强网络对息肉细节的处理能力,细化边界分割效果,提高小尺寸息肉捕获能力。最后,将Transformer和卷积提取的特征进行深度融合,实现特征互补。将该算法在CVC-ClinicDB和Kvasir-SEG数据集上进行实验,其相似性系数分别为95.4%和93.2%,平均交并比分别为91.3%和88.6%。进一步在CVC-ColonDB、CVC-T和ETIS数据集上测试泛化能力,其相似性系数分别为81.3%、90.9%和80.1%。结果表明,所提算法在息肉分割的准确度上有较大的提升。

In response to the challenges of varied sizes and diverse shapes of colorectal polyps,especially with blurred boundaries that often complicates localization and smaller polyps being particularly prone to oversight,a colorectal polyp segmentation algorithm integrating Transformer and convolution is proposed.Transformer is employed to extract global features from images for ensuring the network's capability for global modeling and improving the localization capability for both main polyp regions and vague boundaries.Subsequently,convolution is introduced to augment the network's ability to process polyp details,refining boundary segmentation and enhancing the capture capability for small-sized polyps.Finally,a deep fusion of the features extracted by Transformer and convolution is carried out to realize feature complementarity.The experimental evaluation using CVC-ClinicDB and Kvasir-SEG datasets show that the algorithm has similarity coefficients of 95.4%and 93.2%,and mean intersection over union of 91.3%and 88.6%,respectively.Further tests on the generalization capability of the algorithm are conducted on CVC-ColonDB,CVC-T,and ETIS datasets,in which similarity coefficients of 81.3%,90.9%and 80.1%are obtained.The results indicate a notable improvement in the accuracy of polyp segmentation achieved by the proposed algorithm.