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融合邊緣信息和雙分支註意力的息肉分割算法

Polyp segmentation algorithm combining boundary knowledge and dual-branch attention

  • 摘要: 針對息肉與其周圍黏膜之間邊界不清晰導致的息肉分割精度低這一問題🤛🏼,提出一種融合邊緣信息和雙分支註意力的息肉分割模型(BDANet),用於在結腸鏡檢查圖像中準確分割息肉🥷🏼。使用邊緣提取感知模塊以同時利用低級細節信息和高級語義信息生成邊緣細節特征,將其與全局特征相融合,生成具有邊界感知意識的全局信息🎃,采用殘差學習結構在不同層級強調邊界學習,由深至淺整合不同層級的側輸出特征👨‍🦼‍➡️,設計雙分支註意力模塊同時從正向和反向學習目標物體的邊界,降低模型對邊界區域的預測不確定性。針對4個息肉分割常用數據集的7個指標的定量和定性評估表明,所提模型BDANet能夠有效提高分割精度。

     

    Abstract: Aiming at the problem of poor precision of polyp segmentation due to unclear boundaries between polyps and the surrounding mucosa, a polyp segmentation model (BDANet) combining boundary knowledge and dual-branch attention is proposed for accurate polyp segmentation in colonoscopy images. A boundary extraction and awareness module is designed to simultaneously utilize low-level detail information and high-level semantic information for generating edge detail features. These features are fused with global features to generate globally aware information with boundary perception. A residual learning structure is adopted to emphasize boundary learning at different levels. Side output features from various levels are integrated from deep to shallow. A dual-branch attention module is designed to learn boundaries of target objects both in forward and reverse directions, reducing the uncertainty of model in predicting boundary regions. Quantitative and qualitative evaluations on 7 metrics of 4 commonly used polyp segmentation datasets demonstrate that the proposed BDANet can effectively improve segmentation accuracy.

     

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