Abstract：
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.
摘要：
不规则解剖结构的定量分析在口腔医学中至关重要，但临床医生通常只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,挑战依然存在,包括由于边界不清楚而导致的分割困难，以及在整个定量分析中获取临床需要的测量标志。以腭牙槽骨为例，我们提出了一种人工智能测量工具，用于整个不规则牙齿结构的定量分析。为了扩大适用性，我们已经包括了具有更少参数和更低计算需求的轻量级网络。我们的方法最终使用了轻量级模型LU-Net，通过补偿模块解决边界不清晰造成的分割挑战。进行额外的牙釉质分割以建立测量坐标系。最终,我们以满足临床需要的方式呈现了结构内的全部定量信息.该工具取得了良好的分割效果，表现为高骰子系数(0.934和0.949)，在联合线上相交(0.888和0.907)，测试集中腭牙槽骨和牙釉质的曲线下面积(0.943和0.949)。在随后的测量中，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，该工具的测量结果显示与地面实况没有统计学上的显著差异，平均绝对误差小，均方根误差，和错误间隔。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。这有助于使临床医生能够迅速全面地掌握结构特征，便于为不同患者设计更个性化的治疗方案，提高临床效率和治疗成功率。
