CMFDNet提升肠镜息肉分割精度，尤其对小尺寸和边界模糊的息肉更有效。
CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation
- 设计跨扫描解码器与多分支结构，增强多形态息肉识别能力
- 在ETIS和ColonDB数据集上mDice分别提升1.83%和1.55%，超越现有最优方法
- 适合医学影像分析、内窥镜图像分割场景，尤其关注小息肉检测
自动化结肠息肉分割对早期癌前病变筛查和结直肠肿瘤诊断至关重要。尽管现有方法已取得良好效果，但息肉分割仍面临三大挑战：(1) 息肉形状与大小差异大；(2) 息肉与周围组织边界模糊；(3) 小尺寸息肉易被忽略。针对这些实际难题，本文提出CMFDNet架构，包含CMD模块、MSA模块和FD模块。CMD模块作为创新解码器，引入跨扫描机制以减少边界模糊；MSA模块采用多分支并行结构，增强对多种几何形态和尺度分布息肉的识别能力；FD模块建立解码器特征间的依赖关系，缓解小尺度特征息肉的漏检问题。实验结果表明，CMFDNet优于六种SOTA方法，尤其在ETIS和ColonDB数据集上，mDice分数分别超过最佳SOTA方法1.83%和1.55%。
原文摘要 · Abstract (English)
Automated colonic polyp segmentation is crucial for assisting doctors in screening of precancerous polyps and diagnosis of colorectal neoplasms. Although existing methods have achieved promising results, polyp segmentation remains hindered by the following limitations,including: (1) significant variation in polyp shapes and sizes, (2) indistinct boundaries between polyps and adjacent tissues, and (3) small-sized polyps are easily overlooked during the segmentation process. Driven by these practical difficulties, an innovative architecture, CMFDNet, is proposed with the CMD module, MSA module, and FD module. The CMD module, serving as an innovative decoder, introduces a cross-scanning method to reduce blurry boundaries. The MSA module adopts a multi-branch parallel structure to enhance the recognition ability for polyps with diverse geometries and scale distributions. The FD module establishes dependencies among all decoder features to alleviate the under-detection of polyps with small-scale features. Experimental results show that CMFDNet outperforms six SOTA methods used for comparison, especially on ETIS and ColonDB datasets, where mDice scores exceed the best SOTA method by 1.83% and 1.55%, respectively.
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