融合Transformer和线索交叉聚合的结直肠息肉分割方法
Colorectal polyp segmentation method fusing Transformer and cross-cue fusion
梁礼明 1李俞霖 1金家新 1何安军 1夏雨辰2
作者信息
- 1. 江西理工大学电气工程与自动化学院,江西赣州 341000
- 2. 江西省通讯终端产业技术研究院有限公司软件部,江西吉安 343000
- 折叠
摘要
针对结直肠息肉图像分割时动态信息处理和边缘细节捕捉能力不足,导致边界信息损失和错误分割等问题,本文提出一种建立在Swin Transformer框架上的线索交叉聚合(cross-cue fu-sion,CCF)结肠息肉分割方法.该方法首先通过Transformer编码器对图像的病变特征进行逐级提取.其次利用改进的二阶通道注意力(second-order channel attention,SOCA)机制增强跨层级信息交互能力,有效提取丰富的多尺度上下文特征信息.再次采用反向通道频率注意力(reverse channel frequency attention,RCFA)机制中的离散余弦变换(discrete cosine transform,DCT),突出多尺度上下文信息的通道特征.最后通过CCF模块从动态和静态深度两个层面增强图像特征,进而提升动态信息处理和细节捕捉能力.在数据集CVC-ClinicDB、Kvasir、CVC-ColonDB和ETIS-LaribPolypDB上进行测试,Dice指数分别为0.942、0.924、0.800和0.774.MIoU指数分别为0.896、0.878、0.726和0.697.实验数据表明,本文提出的方法能有效分割结直肠息肉图像,为结直肠息肉的诊断提供了新思路.
Abstract
In order to solve the problems of insufficient dynamic information processing and edge detail capture in colorectal polyp image segmentation,such as boundary information loss and wrong segmentation,this paper proposes a colorectal polyp segmentation method based on Swin Transformer framework.Firstly,Transformer encoder is used to extract the pathological features of the image step by step.Secondly,the improved second-order channel attention(SOCA)mechanism is used to enhance cross-level information interaction ability and effectively extract rich multi-scale context feature information.Furthermore,the discrete cosine transform(DCT)in the attention mechanism of reverse frequency channel is used to highlight the channel characteristics of multi-scale context information.Finally,the image features are enhanced from both dynamic and static depth through the cross-cue fusion(CCF)module to improve the dynamic information processing and detail capture capabilities.When tested on the datasets CVC-ClinicDB,Kvasir,CVC-ColonDB,and ETIS-LaribPolypDB,Dice indices are 0.942,0.924,0.800 and 0.774,respectively.The MIoU indices are 0.896,0.878,0.726 and 0.697,respectively.The experimental data show that the proposed method can effectively segment colorectal polyp images and provide a new idea for the diagnosis of colorectal polyp.
关键词
图像分割/结直肠息肉/Transformer/线索交叉聚合(CCF)模块/反向通道频率注意力(RCFA)模块Key words
image segmentation/colorectal polyps/Transformer/cross-cue fusion(CCF)module/reverse channel frequency attention(RCFA)module引用本文复制引用
出版年
2025
NETL
NSTL
万方数据