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GCNet: Global Context-Guided Uncertainty Boundary for Polyp Segmentation

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15045))

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Abstract

Colorectal cancer, with the third highest incidence and mortality rates, underscores the importance of accurately segmenting polyps in colonoscopy images. Despite advancements in deep learning-based methods, several challenges persist: (1) The uneven surface of the colon wall introduces significant background noise in images; (2) The varied sizes of colonic polyps make detection difficult; (3) The potential of cross-layer features is not fully harnessed. Addressing these issues, we propose the Global context-guided uncertainty boundary for polyp segmentation (GCNet). Our method leverages cross-layer features for both boundary and global context extraction, enhancing its expressive capabilities. The Global Context Extraction Module (GCEM) obtains global context with different polyp sizes. Concurrently, the boundary Extraction Module (BEM) is capable of obtaining accurate boundaries in the presence of a large amount of background noise. Moreover, boundary information and residual information enhanced via the Uncertainty Residual Attention Module (URAM) are incorporated into the network to generate finer segmentation maps. Experimental results on five public datasets demonstrate that the proposed GCNet outperforms recent state-of-the-art competing methods. All code is available at https://github.com/dxqllp/GCNet.

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Acknowledgments

The work was supported by Hefei Municipal Natural Science Foundation (2022009) and the High-performance Computing Platform of Anhui University for providing computing resources.

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China

    Xuejun Zhang, Jiajia Chen, Jie Gui, Xiuquan Du & Wen Sha

Authors
  1. Xuejun Zhang
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  2. Jiajia Chen
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  3. Jie Gui
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  4. Xiuquan Du
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  5. Wen Sha
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Corresponding author

Correspondence to Xiuquan Du.

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Urumqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Urumqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Zhang, X., Chen, J., Gui, J., Du, X., Sha, W. (2025). GCNet: Global Context-Guided Uncertainty Boundary for Polyp Segmentation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15045. Springer, Singapore. https://doi.org/10.1007/978-981-97-8499-8_14

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