基于多尺度特征和上下文聚合的结肠息肉图像分割网络  

Colon Polyp Image Segmentation Network Based on Multi-scale Features and Contextual Aggregation

作　　者：许海英 徐健皓 陈平华[1] XU Hai-Ying;XU Jian-Hao;CHEN Ping-Hua(School of Computer,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《计算机系统应用》2025年第3期115-123,共9页Computer Systems & Applications

基　　金：广东省重点领域研发计划(2023B1111050010,2020B0101100001)。

摘　　要：为解决结肠息肉图像语义分割任务中存在的边界不清晰以及分割结果不连贯、不完整甚至丢失的问题,提出一种基于多尺度特征和上下文聚合的结肠息肉图像分割网络(colon polyp image segmentation network based on multi-scale features and contextual aggregation,MFCA-Net).网络选择PvTv2作为特征提取主干网络,设计多尺度特征互补模块(MFCM)用以提取丰富的多尺度局部信息,减少息肉形态变化对分割结果的影响;设计全局信息增强模块(GIEM),构建嵌入位置注意力的大核深度卷积实现对息肉的精确定位,提升网络辨别复杂背景的能力;设计高级语义引导的上下文聚合模块(HSCAM),以全局特征引导局部特征,差异性互补和交叉融合浅层细节信息与深层语义信息,提升分割的连贯性和完整性;设计边界感知模块(BPM),结合传统图像处理方法与深度学习方法优化边界特征,实现细粒度分割,进而获取更清晰的边界.实验表明,在Kvasir、ClinicDB、ColonDB和ETIS等公开的结肠息肉图像数据集上,所提出的网络均取得相较于当前主流算法更高的mDice与mIoU分数,具有更高的分割准确率和更强的鲁棒性.To solve unclear boundaries and incoherent,incomplete,or even lost segmentation results in the semantic segmentation task of colon polyp images,a colon polyp image segmentation network named colon polyp image segmentation network based on multi-scale features and contextual aggregation(MFCA-Net)is proposed.The network selects PvTv2 as the backbone network for feature extraction.The multi-scale feature complement module(MFCM)is designed to extract rich multi-scale local information and reduce the influence of polyp morphology changes on segmentation results.The global information enhancement module(GIEM)is designed.A large-kernel deep convolution embedded with positional attention is constructed to accurately locate polyps and improve the network’s ability to distinguish complex backgrounds.The high-level semantic-guided context aggregation module(HSCAM)is designed.It guides local features with global features,complements differences,and cross-fuses shallow details and deep semantic information to improve the coherence and integrity of segmentation.The boundary perception module(BPM)is designed.Boundary features are optimized by combining traditional image processing methods and deep learning methods to achieve fine-grained segmentation and obtain clearer boundaries.Experiments show that the proposed network obtains higher mDice and mIoU scores compared with current mainstream algorithms on the publicly available colon polyp image datasets such as Kvasir,ClinicDB,ColonDB,and ETIS,and has higher segmentation accuracy and robustness.

关 键 词：结肠息肉图像分割 多尺度特征 上下文聚合 TRANSFORMER 注意力机制 

分 类 号：R735.34[医药卫生—肿瘤] TP391.41[医药卫生—临床医学]