This paper proposed a cutting-edge multiclass teeth segmentation architecture that integrates an M-Net-like structure with Swin Transformers and a novel component named Teeth Attention Block (TAB). Existing teeth image segmentation methods have issues with less accurate and unreliable segmentation outcomes due to the complex and varying morphology of teeth, although teeth segmentation in dental panoramic images is essential for dental disease diagnosis. We propose a novel teeth segmentation model incorporating an M-Net-like structure with Swin Transformers and TAB. The proposed TAB utilizes a unique attention mechanism that focuses specifically on the complex structures of teeth. The attention mechanism in TAB precisely highlights key elements of teeth features in panoramic images, resulting in more accurate segmentation outcomes. The proposed architecture effectively captures local and global contextual information, accurately defining each tooth and its surrounding structures. Furthermore, we employ a multiscale supervision strategy, which leverages the left and right legs of the U-Net structure, boosting the performance of the segmentation with enhanced feature representation. The squared Dice loss is utilized to tackle the class imbalance issue, ensuring accurate segmentation across all classes. The proposed method was validated on a panoramic teeth X-ray dataset, which was taken in a real-world dental diagnosis. The experimental results demonstrate the efficacy of our proposed architecture for tooth segmentation on multiple benchmark dental image datasets, outperforming existing state-of-the-art methods in objective metrics and visual examinations. This study has the potential to significantly enhance dental image analysis and contribute to advances in dental applications.

翻译：本文提出了一种前沿的多类牙齿分割架构，该架构将类M-Net结构与Swin变换器及名为牙齿注意力块（TAB）的新型组件相结合。尽管在牙科全景图像中进行牙齿分割对牙科疾病诊断至关重要，但现有牙齿图像分割方法因牙齿形态复杂多变，存在分割结果不准确且不可靠的问题。我们提出了一种新颖的牙齿分割模型，融合了类M-Net结构、Swin变换器与TAB。所提出的TAB利用独特的注意力机制，专门关注牙齿的复杂结构。该注意力机制能精确突出全景图像中牙齿特征的关键元素，从而获得更准确的分割结果。该架构有效捕获局部与全局上下文信息，精确界定每颗牙齿及其周围结构。此外，我们采用多尺度监督策略，利用U-Net结构的左右支路，通过增强特征表示提升分割性能。采用平方Dice损失函数解决类别不平衡问题，确保所有类别的准确分割。该方法在真实牙科诊断中获取的全景牙齿X光数据集上进行了验证。实验结果表明，本文提出的架构在多个基准牙科图像数据集上对牙齿分割具有有效性，在客观指标和视觉检查方面均优于现有最先进方法。本研究有望显著提升牙科图像分析水平，并推动牙科应用领域的进步。