Edge-aware Feature Aggregation Network for Polyp Segmentation

TL;DR

This paper introduces EFA-Net, a novel neural network architecture that enhances polyp segmentation by integrating edge-aware guidance, scale-aware convolutions, and cross-level feature fusion, leading to improved accuracy across multiple datasets.

Contribution

The paper proposes EFA-Net with innovative modules for edge guidance, scale awareness, and feature fusion, advancing the state-of-the-art in polyp segmentation performance.

Findings

EFA-Net outperforms existing methods on five colonoscopy datasets.

The edge-aware guidance improves boundary delineation.

Scale-aware convolutions effectively handle size variation of polyps.

Abstract

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. Our implementation code and segmentation maps will be publicly at https://github.com/taozh2017/EFANet.