A solution for the Semi-supervised Teeth Segmentation and Registration (STSR) 2025 Challenge, which focused on the precise segmentation of teeth and pulp root canals in 3D Cone Beam Computed Tomography (CBCT) scans is presented in this paper. Accurate segmentation of the pulp root canal is crucial for clinical visualization and treatment planning, but manual annotation is extremely labor-intensive. The presented approach uses a semi-supervised framework powered by nnU-Net, leveraging a small labeled dataset of 30 scans alongside a much larger unlabeled dataset of 300 scans. To effectively utilize the unlabeled data, pseudo-labeling was employed to generate annotations, and the model was subsequently trained. The results for both tooth and pulp structures yield a Dice score of 0.8088 and an mIoU of 0.9638 in the all-data track, while the Dice score in the coreset track is 0.69. These metrics highlight the model’s ability to accurately identify and delineate the target structures.