提出新型网络提升肠镜中息肉分割精度，尤其擅长识别小息肉和模糊边界。
MPCGNet: A Multiscale Feature Extraction and Progressive Feature Aggregation Network Using Coupling Gates for Polyp Segmentation
- 用耦合门机制分层提取特征并过滤噪声，增强小息肉信号。
- 在两个公开数据集上分割准确率领先第二名2.20%和0.68%。
- 适合内窥镜图像分割任务，对低对比度、小尺寸病变有显著优势。
自动息肉分割对结直肠息肉筛查与癌症诊断具有重要意义。尽管现有方法取得进展，仍面临三大挑战：（1）小尺寸息肉易被漏检；（2）息肉与周围组织边界模糊；（3）肠镜图像受光照不均等影响产生噪声。为此，本文引入耦合门作为模块组件，用于噪声过滤与特征重要性选择。提出三个模块：耦合门多尺度特征提取（CGMFE）模块，有效提取局部特征并抑制噪声；窗口交叉注意力（WCAD）解码器模块，在定位息肉后恢复细节；解码器特征聚合（DFA）模块，逐级聚合与再提取特征，并进行重要性选择，减少小息肉信息丢失。实验表明，MPCGNet在ETIS-LaribPolypDB和CVC-ColonDB数据集上，mDice分别比第二好模型高2.20%和0.68%。
原文摘要 · Abstract (English)
Automatic segmentation methods of polyps is crucial for assisting doctors in colorectal polyp screening and cancer diagnosis. Despite the progress made by existing methods, polyp segmentation faces several challenges: (1) small-sized polyps are prone to being missed during identification, (2) the boundaries between polyps and the surrounding environment are often ambiguous, (3) noise in colonoscopy images, caused by uneven lighting and other factors, affects segmentation results. To address these challenges, this paper introduces coupling gates as components in specific modules to filter noise and perform feature importance selection. Three modules are proposed: the coupling gates multiscale feature extraction (CGMFE) module, which effectively extracts local features and suppresses noise; the windows cross attention (WCAD) decoder module, which restores details after capturing the precise location of polyps; and the decoder feature aggregation (DFA) module, which progressively aggregates features, further extracts them, and performs feature importance selection to reduce the loss of small-sized polyps. Experimental results demonstrate that MPCGNet outperforms recent networks, with mDice scores 2.20% and 0.68% higher than the second-best network on the ETIS-LaribPolypDB and CVC-ColonDB datasets, respectively.
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