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<p>The rise in brain tumor incidence due to the global population aging has intensified the need for precise segmentation methods in clinical settings. Current segmentation networks often fail to capture comprehensive contextual information and fine edge details of brain tumors, which are crucial for accurate diagnosis and treatment. To address these challenges, we introduce BSAU-Net, a novel segmentation algorithm that employs attention mechanisms and edge feature extraction modules to enhance performance. Our approach aims to assist clinicians in making more accurate diagnostic and therapeutic decisions. BSAU-Net incorporates an edge feature extraction module (EA) based on the Sobel operator, enhancing the model’s sensitivity to tumor regions while preserving edge contours. Additionally, a spatial attention module (SPA) is introduced to establish global feature correlations, critical for accurate tumor segmentation. To address class imbalance, which can hinder performance, we propose BADLoss, a loss function tailored to mitigate this issue. Experimental results on the BraTS2018 and BraTS2021 datasets demonstrate the effectiveness of BSAU-Net, achieving average Dice coefficients of 0.7506 and 0.7556, PPV of 0.7863 and 0.7843, sensitivity of 0.8998 and 0.9017, and HD95 of 2.1701 and 2.1543, respectively. These results highlight BSAU-Net’s potential to significantly improve brain tumor segmentation in clinical practice.</p>

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A brain tumor segmentation method based on attention mechanism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月30日

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  • Juan Cao,
  • Jinjia Liu,
  • Jiaran Chen

摘要

The rise in brain tumor incidence due to the global population aging has intensified the need for precise segmentation methods in clinical settings. Current segmentation networks often fail to capture comprehensive contextual information and fine edge details of brain tumors, which are crucial for accurate diagnosis and treatment. To address these challenges, we introduce BSAU-Net, a novel segmentation algorithm that employs attention mechanisms and edge feature extraction modules to enhance performance. Our approach aims to assist clinicians in making more accurate diagnostic and therapeutic decisions. BSAU-Net incorporates an edge feature extraction module (EA) based on the Sobel operator, enhancing the model’s sensitivity to tumor regions while preserving edge contours. Additionally, a spatial attention module (SPA) is introduced to establish global feature correlations, critical for accurate tumor segmentation. To address class imbalance, which can hinder performance, we propose BADLoss, a loss function tailored to mitigate this issue. Experimental results on the BraTS2018 and BraTS2021 datasets demonstrate the effectiveness of BSAU-Net, achieving average Dice coefficients of 0.7506 and 0.7556, PPV of 0.7863 and 0.7843, sensitivity of 0.8998 and 0.9017, and HD95 of 2.1701 and 2.1543, respectively. These results highlight BSAU-Net’s potential to significantly improve brain tumor segmentation in clinical practice.