Colorectal polyp segmentation method based on fusion of transformer and cross-level phase awareness

针对结肠息肉图像分割时空间归纳偏差和全局上下文信息的有效表示缺失，导致边缘细节信息丢失和病变区域误分割等问题，提出一种融合Transformer和跨级相位感知的结肠息肉分割方法。该方法一是从变换的全局特征角度出发，运用分层Transformer编码器逐层提取病变区域的语义信息和空间细节；二是通过相位感知融合模块（PAFM）捕获各阶段跨层次交互信息，有效聚合多尺度上下文信息；三是设计位置导向功能模块（POF）有效整合全局与局部特征信息，填补语义空白，抑制背景噪声；四是利用残差轴反向注意力模块（RA-IA）来提升网络对边缘像素点的识别能力。在公共数据集CVC-ClinicDB、Kvasir、CVC-ColonDB和EITS上进行实验测试，其Dice相似性系数分别为94.04%、92.04%、80.78%和76.80%，平均交并比分别为89.31%、86.81%、73.55%和69.10%。仿真实验结果表明，本文提出的方法能有效地分割结肠息肉图像，为结直肠息肉的诊断提供了新窗口。.; In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.