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.

本研究提出了一种基于深度学习的方法，利用两个卷积神经网络：SpatialConfiguration-Net (SCN)和修改版的U-Net，精确地预测了CBCT图像中所有牙齿的坐标，从而实现了对牙齿体积的精确裁剪，并通过分割方法检测出病变，解决了CBCT图像中的类别不平衡问题。经过对144个CBCT图像的评估，我们的方法实现了97.3％的牙齿定位准确率，并具有0.97的敏感性和0.88的特异性，用于随后的病变检测。

CBCT图像中的牙齿定位和损伤分割：基于SpatialConfiguration-Net和U-Net