The localization of teeth and segmentation of periapical lesions in cone-beam computed tomography (CBCT) images are crucial tasks for clinical diagnosis and treatment planning, which are often time-consuming and require a high level of expertise. However, automating these tasks is challenging due to variations in shape, size, and orientation of lesions, as well as similar topologies among teeth. Moreover, the small volumes occupied by lesions in CBCT images pose a class imbalance problem that needs to be addressed. In this study, we propose a deep learning-based method utilizing two convolutional neural networks: the SpatialConfiguration-Net (SCN) and a modified version of the U-Net. The SCN accurately predicts the coordinates of all teeth present in an image, enabling precise cropping of teeth volumes that are then fed into the U-Net which detects lesions via segmentation. To address class imbalance, we compare the performance of three reweighting loss functions. After evaluation on 144 CBCT images, our method achieves a 97.3% accuracy for teeth localization, along with a promising sensitivity and specificity of 0.97 and 0.88, respectively, for subsequent lesion detection.

翻译：锥形束计算机断层扫描（CBCT）图像中的牙齿定位与根尖周病变分割是临床诊断和治疗规划的关键任务，这些任务通常耗时且需要高水平专业知识。然而，由于病变形状、大小和方向的差异以及牙齿间相似的拓扑结构，实现这些任务的自动化充满挑战。此外，病变在CBCT图像中所占体积较小导致的类别不平衡问题亟需解决。本研究提出一种基于深度学习的方法，采用两种卷积神经网络：空间配置网络（SCN）和改进版U-Net。SCN可准确预测图像中所有牙齿的坐标，实现牙齿体积的精密切割，随后将切割结果输入U-Net进行病变分割检测。为应对类别不平衡，我们比较了三种重加权损失函数的性能。在144张CBCT图像上的评估结果显示，本方法的牙齿定位准确率达到97.3%，后续病变检测的敏感度和特异度分别达到0.97和0.88，表现出良好的性能。