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Cross-level Feature Aggregation Network for Polyp Segmentation
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DOI：10.1016/j.patcog.2023.109555.png)
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Abstract
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Accurate segmentation of polyps from colonoscopy images plays a critical role in the diagnosis and cure of colorectal cancer. Although effectiveness has been achieved in the field of polyp segmentation, there are still several challenges. Polyps often have a diversity of size and shape and have no sharp bound-ary between polyps and their surrounding. To address these challenges, we propose a novel Cross-level Feature Aggregation Network (CFA-Net) for polyp segmentation. Specifically, we first propose a boundary prediction network to generate boundary-aware features, which are incorporated into the segmentation network using a layer-wise strategy. In particular, we design a two-stream structure based segmentation network, to exploit hierarchical semantic information from cross-level features. Furthermore, a Cross-level Feature Fusion (CFF) module is proposed to integrate the adjacent features from different levels, which can characterize the cross-level and multi-scale information to handle scale variations of polyps. Further, a Boundary Aggregated Module (BAM) is proposed to incorporate boundary information into the segmen-tation network, which enhances these hierarchical features to generate finer segmentation maps. Quan-titative and qualitative experiments on five public datasets demonstrate the effectiveness of our CFA-Net against other state-of-the-art polyp segmentation methods. The source code and segmentation maps will be released at https://github.com/taozh2017/CFANet .(c) 2023 Elsevier Ltd. All rights reserved.
Keywords:
Polyp segmentation
boundary-aware features
cross-level feature fusion
boundary aggregated module
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7.6
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4.5W
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