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<p>Accurate medical image segmentation plays a vital role in assisting diagnosis with quantifiable visual evidence. Due to the complex structure and diverse patterns in medical images, it is crucial to capture both short and long-range pixel relations. While transformers are adept at modeling long-range spatial dependencies in images, they struggle with learning local pixel relationships. To address this, we propose a deep learning network named GoLoCo-Net incorporating a dual decoder structure. More specifically, one decoder entails a Contextual Attention Feature Enhancement (CAFE) module to enhance the features for a broader capture of local and global contexts, whereas the other uses a Global-Guide-Local Feature (GGLF) module that leverages high-level features to enrich low-level features with a global context. The proposed method is evaluated on two dynamic MRI datasets and one multi-organ CT dataset. Experimental results show that the model achieves state-of-the-art performance across all three datasets. The code is available:<a href="https://github.com/Yhe9718/GoLoCoNet">https://github.com/Yhe9718/GoLoCoNet</a>.</p>

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GoLoCo-Net: global-local guided contextual attention network for medical images segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月5日

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  • Ying He,
  • Marc E. Miquel,
  • Qianni Zhang

摘要

Accurate medical image segmentation plays a vital role in assisting diagnosis with quantifiable visual evidence. Due to the complex structure and diverse patterns in medical images, it is crucial to capture both short and long-range pixel relations. While transformers are adept at modeling long-range spatial dependencies in images, they struggle with learning local pixel relationships. To address this, we propose a deep learning network named GoLoCo-Net incorporating a dual decoder structure. More specifically, one decoder entails a Contextual Attention Feature Enhancement (CAFE) module to enhance the features for a broader capture of local and global contexts, whereas the other uses a Global-Guide-Local Feature (GGLF) module that leverages high-level features to enrich low-level features with a global context. The proposed method is evaluated on two dynamic MRI datasets and one multi-organ CT dataset. Experimental results show that the model achieves state-of-the-art performance across all three datasets. The code is available:https://github.com/Yhe9718/GoLoCoNet.