Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net
This addresses a time-consuming and expertise-intensive task in clinical dentistry, offering an incremental improvement through a hybrid deep learning approach.
The study tackled the problem of automating teeth localization and periapical lesion segmentation in CBCT images, achieving 97.3% accuracy for teeth localization and promising sensitivity and specificity of 0.97 and 0.88 for lesion detection.
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.