Quantitative analysis of irregular anatomical structures is crucial in oral medicine, but clinicians often typically measure only several representative indicators within the structure as references. Deep learning semantic segmentation offers the potential for entire quantitative analysis. However, challenges persist, including segmentation difficulties due to unclear boundaries and acquiring measurement landmarks for clinical needs in entire quantitative analysis. Taking the palatal alveolar bone as an example, we proposed an artificial intelligence measurement tool for the entire quantitative analysis of irregular dental structures. To expand the applicability, we have included lightweight networks with fewer parameters and lower computational demands. Our approach finally used the lightweight model LU-Net, addressing segmentation challenges caused by unclear boundaries through a compensation module. Additional enamel segmentation was conducted to establish a measurement coordinate system. Ultimately, we presented the entire quantitative information within the structure in a manner that meets clinical needs. The tool achieved excellent segmentation results, manifested by high Dice coefficients (0.934 and 0.949), intersection over union (0.888 and 0.907), and area under the curve (0.943 and 0.949) for palatal alveolar bone and enamel in the test set. In subsequent measurements, the tool visualizes the quantitative information within the target structure by scatter plots. When comparing the measurements against representative indicators, the tool’s measurement results show no statistically significant difference from the ground truth, with small mean absolute error, root mean squared error, and errors interval. Bland-Altman plots and intraclass correlation coefficients indicate the satisfactory agreement compared with manual measurements. We proposed a novel intelligent approach to address the entire quantitative analysis of irregular image structures in the clinical setting. This contributes to enabling clinicians to swiftly and comprehensively grasp structural features, facilitating the design of more personalized treatment plans for different patients, enhancing clinical efficiency and treatment success rates in turn.

中文翻译：

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智能量化整个不规则牙齿结构

不规则解剖结构的定量分析在口腔医学中至关重要，但临床医生通常仅测量结构内的几个代表性指标作为参考。深度学习语义分割为整个定量分析提供了潜力。然而，挑战仍然存在，包括由于边界不明确而导致的分割困难以及在整个定量分析中获取临床需求的测量标志。以腭牙槽骨为例，我们提出了一种对不规则牙齿结构进行全程定量分析的人工智能测量工具。为了扩展适用性，我们引入了参数较少、计算需求较低的轻量级网络。我们的方法最终使用了轻量级模型 LU-Net，通过补偿模块解决了边界不清晰带来的分割挑战。进行额外的牙釉质分割以建立测量坐标系。最终，我们以满足临床需求的方式呈现了结构内的全部定量信息。该工具取得了出色的分割结果，表现在测试集中的腭牙槽骨和牙釉质的高 Dice 系数（0.934 和 0.949）、并集交集（0.888 和 0.907）以及曲线下面积（0.943 和 0.949）。在后续测量中，该工具通过散点图可视化目标结构内的定量信息。当将测量结果与代表性指标进行比较时，该工具的测量结果与真实情况没有显着的统计差异，平均绝对误差、均方根误差和误差区间都较小。Bland-Altman 图和组内相关系数表明与手动测量相比具有令人满意的一致性。我们提出了一种新颖的智能方法来解决临床环境中不规则图像结构的整个定量分析。这有助于临床医生快速、全面地掌握结构特征，有利于针对不同患者设计更加个性化的治疗方案，从而提高临床效率和治疗成功率。