Abstract: Oral diseases seriously affect public health, and timely and effective diagnosis and treatment are of great significance for reducing the risk of disease progression. Conventional diagnosis of oral diseases mainly relies on manual interpretation of imaging data by experienced clinicians, which is often time-consuming and may overlook lesions with blurred boundaries. Therefore, image segmentation techniques are needed to assist the clinical diagnosis of dental diseases. Dental panoramic radiographs can present the overall morphology of teeth and jawbone structures in a single image and are commonly used in clinical dental diagnosis. However, due to low gray-level contrast, blurred lesion boundaries, noise, and artifact interference commonly present in dental panoramic radiographs, multi-class dental disease segmentation, including dental caries, periapical periodontitis, furcation involvement, and impacted teeth, remains highly challenging. To address these issues, this paper proposes Teeth-Net, a network for multi-class dental disease segmentation in dental panoramic radiographs. Based on the TransUNet architecture, Teeth-Net introduces targeted improvements in three key stages: feature extraction, feature reconstruction, and skip connections. In the feature extraction stage, a Cross-Scale Pyramid Fusion Module (CPFM) is introduced to optimize the original encoder. Multi-scale features are extracted through parallel group convolutions with different receptive fields, and the correlations among features at different scales are modeled using a cross-scale attention mechanism, thereby enhancing the model’s ability to capture small lesions and alleviating the loss of detailed features. In the feature reconstruction stage, a Parallel Multi-Kernel Pooling Module (PMKP) is designed to extract local details and global contextual information in parallel through multi-scale max pooling and average pooling. Channel compression and feature fusion are then performed to provide richer semantic information for the decoder. At each skip connection, a Spatial-Channel Collaborative Attention module (SCCA) is embedded to adaptively filter shallow features transmitted from the encoder through spatial and channel attention mechanisms, suppress background noise interference, and improve the quality of cross-layer feature fusion between the encoder and decoder. Comparative and ablation experiments are conducted on a self-built dental panoramic radiograph dataset. The experimental results show that Teeth-Net achieves a mean Dice coefficient, Hausdorff Distance (HD), precision, and recall of 84.22%, 18.546 mm, 94.13%, and 95.96%, respectively. Compared with the baseline TransUNet model, the mean Dice coefficient, precision, and recall are improved by 3.34, 2.89, and 4.21 percentage points, respectively, while the HD value is reduced by 6.869 mm. These results indicate that the proposed method achieves significant improvements in overall segmentation accuracy, boundary consistency, and lesion detection capability. To further evaluate the generalization ability and cross-dataset adaptability of the model, external tests are conducted on two public-source datasets. On the re-annotated MICCAI 2023 STS external test set, Teeth-Net achieves a mean Dice coefficient, HD value, precision, and recall of 80.26%, 19.520 mm, 92.58%, and 93.41%, respectively. Compared with the baseline TransUNet model, the mean Dice coefficient, precision, and recall are improved by 3.32, 4.33, and 3.89 percentage points, respectively, while the HD value is reduced by 6.705 mm. On the public Multi-Center Dental Panoramic Radiography Image (MCDP) dataset, Teeth-Net achieves a mean Dice coefficient, HD value, precision, and recall of 88.99%, 12.126 mm, 90.61%, and 92.45%, respectively. Compared with the baseline TransUNet model, the mean Dice coefficient, precision, and recall are improved by 3.83, 4.03, and 3.33 percentage points, respectively, while the HD value is reduced by 7.222 mm. The results on the self-built dataset and the two external test datasets demonstrate that Teeth-Net achieves better segmentation accuracy, boundary delineation ability, and cross-domain adaptability than the baseline TransUNet model under different data sources and imaging conditions. The proposed method can provide effective technical support for the assisted diagnosis of multi-class dental diseases in dental panoramic radiographs.

摘要： 口腔疾病严重影响民众健康，及时且有效的诊断与治疗对降低口腔疾病恶化风险具有重要意义。传统口腔疾病诊断依赖经验丰富的医生对影像资料进行人工判读，存在诊断耗时较长以及边界模糊病症易漏诊等问题，因此需要借助图像分割技术辅助临床牙齿病症的诊断。口腔全景片能够在单幅图像中呈现牙齿整体形态和颌骨结构，是临床牙科诊断中常用的医学影像资料。然而，由于口腔全景片中普遍存在灰度对比度低、病症边界模糊、噪声和伪影干扰等问题，龋齿、牙根尖周炎、根分叉病变和阻生齿等多类别牙齿病症分割仍面临较大挑战。针对上述问题，提出一种面向口腔全景片多类别牙齿病症分割网络Teeth-Net。该网络以TransUNet结构为基础，并在特征提取、特征重建和跳跃连接三个关键阶段进行针对性改进。在特征提取阶段引入跨尺度金字塔融合模块(Cross-Scale Pyramid Fusion Module, CPFM)优化原有编码器，通过不同感受野的并行群卷积提取多尺度特征，并利用跨尺度注意力机制建模不同尺度特征之间的相关性，从而增强模型对细小病症的捕捉能力，缓解细节特征丢失；在特征重建阶段设计并行多核池化模块(Parallel Multi-Kernel Pooling Module, PMKP)，通过多尺度最大池化与平均池化并行提取局部细节和全局上下文信息，并经过通道压缩与特征融合为解码器提供更丰富的语义信息；在各级跳跃连接处嵌入空域-通道协同注意力模块(Spatial-Channel Collaborative Attention, SCCA)，通过空间与通道注意力机制对编码器传递的浅层特征进行自适应筛选，抑制背景噪声的干扰，提高编码器与解码器之间的跨层特征融合质量。在自建口腔全景片数据集上进行对比实验与消融实验。实验结果表明，Teeth-Net的平均Dice系数、豪斯多夫距离（Hausdorff Distance, HD）、精确率和召回率分别达到84.22%、18.546mm、94.13%和95.96%。与基线模型TransUNet相比，平均Dice系数、精确率和召回率分别提升3.34、2.89和4.21个百分点，HD值降低6.869mm，表明该方法在整体分割精度、边界一致性和病症检出能力方面均取得明显改善。为进一步评估模型的泛化能力与跨数据集适应性，在两个公开来源数据集上开展外部测试。在重新标注的MICCAI 2023 STS外部测试集上，Teeth-Net的平均Dice系数、HD值、精确率和召回率分别为80.26%、19.520mm、92.58%和93.41%。与基线模型TransUNet相比，平均Dice系数、精确率和召回率分别提升3.32、4.33和3.89个百分点，HD值降低6.705mm。在公开多中心牙科全景影像数据集(Multi-Center Dental Panoramic Radiography Image, MCDP)上，Teeth-Net的平均Dice系数、HD值、精确率和召回率分别达到88.99%、12.126mm、90.61%和92.45%。与基线模型TransUNet相比，平均Dice系数、精确率和召回率分别提升3.83、4.03和3.33个百分点，HD值降低7.222mm。综合自建数据集和两组外部测试结果可知，Teeth-Net相较于基线模型TransUNet在不同数据来源和成像条件下均表现出更好的分割精度、边界刻画能力和跨域适应性，可为口腔全景片中多类别牙齿病症的辅助诊断提供有效的技术支持。