  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.
