Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.

翻译：精确的息肉分割对于临床实践中结直肠癌的早期诊断与预防至关重要。然而，由于尺度变化和模糊的息肉边界，在应对不同尺度和形状时，实现令人满意的分割性能仍然是一项具有挑战性的任务。本研究提出了一种新颖的边缘感知特征聚合网络（EFA-Net）用于息肉分割，该网络能够充分利用跨层级和多尺度特征，以提升息肉分割的性能。具体而言，我们首先设计了一个边缘感知引导模块（EGM），将低层特征与高层特征相结合，学习得到边缘增强特征，并通过逐层策略将其融入每个解码单元。此外，我们提出了一个尺度感知卷积模块（SCM），通过使用不同膨胀率的空洞卷积来学习尺度感知特征，以有效应对尺度变化。进一步地，我们提出了一个跨层级融合模块（CFM），通过有效整合跨层级特征，从而利用局部和全局上下文信息。最后，利用学习到的边缘感知特征对CFM的输出进行自适应加权，并据此生成多个侧输出分割图。在五个广泛采用的结肠镜数据集上的实验结果表明，我们的EFA-Net在泛化能力和有效性方面均优于现有的最先进息肉分割方法。