Missing Tooth Height Map Prediction via CBAM-Enhanced Conditional Pix2Pix with Sobel Edge Loss

Personalized reconstruction of missing-tooth morphology is a key problem in digital prosthodontics. The main challenge is to generate results that are consistent with the patient’s local dentition, the morphology of the contralateral teeth, and anatomically plausible occlusal details. Although several deep learning-based methods have been proposed for dental restoration, existing approaches still have limitations, including insufficient use of patient-specific contextual information, oversmoothed boundary structures in the generated results, and relatively high model complexity. To address these limitations, this study proposes a CBAM-Sobel conditional Pix2Pix framework, termed CS-cPix2Pix, for predicting the height map of a missing tooth from height projection maps of the contralateral teeth and adjacent teeth. The framework uses height projection maps of a three-tooth contralateral region and an adjacent-tooth region as conditional inputs. A U-Net generator is adopted to learn the mapping from the input conditions to the target missing-tooth height map, and a convolutional block attention module is introduced in the encoder to enhance feature representation in key morphological regions. Furthermore, a Sobel edge loss is incorporated in addition to the adversarial loss and L1 reconstruction loss to constrain the local gradient structure of the generated height map and reduce oversmoothing of occlusal edges, grooves, and ridges. Experimental results show that CS-cPix2Pix achieves better overall quantitative performance than the baseline Pix2Pix model and multiple ablation models, especially in terms of PSNR, FSIM, IoU, and Sobel-L1. Under the current experimental setting, the proposed method generates missing-tooth height maps with clearer boundaries and more continuous structures, and it supports relatively stable reconstruction of three-dimensional occlusal surface meshes from the predicted height maps. However, the present model development still mainly relies on a single public orthodontic dental dataset and focuses primarily on teeth numbered 4, 5, and 6. Therefore, the generalization of the proposed method to other tooth positions, other scanners, different populations, and different acquisition conditions still requires further verification.