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结合注意力机制和邻域增强机制的牙颌模型分割网络

Segmentation Network of Dental Mesh Model Based on Attention Mechanism and Neighbor Enhancement

  • 摘要: 针对现有牙齿模型深度学习分割方法过度依赖坐标特征, 并忽略了几何特征与坐标特征的差异性和互补作用的问题, 提出一种结合注意力机制和邻域增强机制的牙颌模型分割网络. 首先, 采用一个注意力增强模块, 通过不同的编码方式对输入特征进行邻域增强, 并结合几何特征与坐标特征的性质分别设计差值注意力机制和自注意力机制充分提取网格的上下文特征; 其次, 构建互补性池化函数融合最大池化, 注意力池化以及平均池化促进不同特征间的互补作用, 提高分割网络的鲁棒性; 最后, 嵌入自适应注意力模块, 在平衡多尺度特征间的数值差异的同时, 获得综合特征来实现网格分割. 在真实患者的牙颌模型数据集上进行的实验证明, 所提出的方法在三维牙颌模型分割方面优于最先进的深度学习方法.

     

    Abstract: To address the issue of existing deep learning-based segmentation methods for dental models, which overly rely on coordinate features and overlook the differences and complementary nature of geometric and coordinate features, a segmentation network based on attention mechanism and neighbor enhancement is proposed. First, an attention-enhanced module is employed to enhance the neighborhood of input features through different encoding schemes. It designs a differential attention mechanism and a self-attention mechanism to extract comprehensive contextual features of the mesh by considering the properties of geometric and coordinate features separately. Next, a complementary pooling function is constructed to fuse maximum pooling, attention pooling, and average pooling, which promotes the complementary interaction among different features and enhances the robustness of the segmentation network. Finally, an adaptive attention module is embedded to balance the numerical differences among multi-scale features for effective mesh segmentation. Experiments conducted on a real patient dataset of dental models demonstrate that the proposed method outperforms state-of-the-art deep learning methods in dental model segmentation.

     

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