[论文解读] MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation

Nhat-Tan Bui, Dinh-Hieu Hoang+3 more|arXiv (Cornell University)|Sep 6, 2023

Colorectal Cancer Screening and Detection被引用 7

一句话总结

MEGANet 引入了多尺度边缘引导注意框架，用于息肉分割，利用高频边缘特征和解码边界/反向注意以在弱边界场景中改善边界 delineation。

ABSTRACT

Efficient polyp segmentation in healthcare plays a critical role in enabling early diagnosis of colorectal cancer. However, the segmentation of polyps presents numerous challenges, including the intricate distribution of backgrounds, variations in polyp sizes and shapes, and indistinct boundaries. Defining the boundary between the foreground (i.e. polyp itself) and the background (surrounding tissue) is difficult. To mitigate these challenges, we propose Multi-Scale Edge-Guided Attention Network (MEGANet) tailored specifically for polyp segmentation within colonoscopy images. This network draws inspiration from the fusion of a classical edge detection technique with an attention mechanism. By combining these techniques, MEGANet effectively preserves high-frequency information, notably edges and boundaries, which tend to erode as neural networks deepen. MEGANet is designed as an end-to-end framework, encompassing three key modules: an encoder, which is responsible for capturing and abstracting the features from the input image, a decoder, which focuses on salient features, and the Edge-Guided Attention module (EGA) that employs the Laplacian Operator to accentuate polyp boundaries. Extensive experiments, both qualitative and quantitative, on five benchmark datasets, demonstrate that our MEGANet outperforms other existing SOTA methods under six evaluation metrics. Our code is available at https://github.com/UARK-AICV/MEGANet.

研究动机与目标

• 在弱边界和复杂粘膜下实现鲁棒的息肉分割的动机。
• 开发一个边缘引导注意机制，在跨尺度上聚焦于息肉边界。
• 利用高频边缘信息和解码注意来提高分割精度。

提出的方法

• 引入一个边缘引导注意（EGA）模块，从拉普拉斯金字塔获取高频特征以引导对边界区域的注意。
• 在每一层利用解码预测特征，将其分解为边界注意和反向注意以细化预测。
• 将边界注意和反向注意结合起来，强调目标区域并抑制背景渗漏。
• 在多尺度编码-解码架构中操作，以在解码器层之间传播边缘引导线索。
• 可视化并利用高频特征，以保留对息肉轮廓至关重要的纹理和边缘细节。

实验结果

研究问题

• RQ1 高频边缘信息如何在弱边界的息肉分割中提升边界精度？
• RQ2 在多尺度上实现的边缘引导注意机制是否能在不同数据集上持续提升息肉边界 delineation？
• RQ3 将解码特征分解为边界注意与反向注意对分割质量有何影响？
• RQ4 基于拉普拉斯边界注意与多尺度解码如何互动以提升整体分割？

主要发现

• 边缘引导注意模块通过来自拉普拉斯金字塔的高频特征突出边界区域，指导解码器层关注息肉边缘。
• 解码特征分解为边界注意和反向注意后，增强了息肉与周围粘膜的区分度。
• 将边界注意与反向注意结合有助于模型识别整个对象区域并抑制背景噪声。
• 该方法通过多尺度引导和边缘信息保留，在弱边界场景中强调边界精度。