Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography.

Recent advancements in deep learning have revolutionized digital dentistry, highlighting the importance of precise dental segmentation. This study leverages active learning with the three-dimensional (3D) nnU-net and multi-labels to improve segmentation accuracy of dental anatomies, including the maxillary sinuses, maxilla, mandible, and inferior alveolar nerves (IAN), which are important for implant planning, in 3D cone-beam computed tomography (CBCT) scans. Segmentation accuracy was compared using single-label, adjacent pair-label, and multi-label relevant anatomic structures with 60 CBCT scans from Kooalldam Dental Hospital and externally validated using data from Seoul National University Dental Hospital. The dataset was divided into three training stages for active learning. The evaluation metrics were assessed through the Dice similarity coefficient (DSC) and mean absolute difference. The overall internal test set DSCs from the multi-label, single-label, and pair-label models were 95%, 91% (paired t-test; p = 0.01), and 93% (p = 0.03), respectively. The DSC of the IAN in the internal and external datasets increased from 83% to 79%, 87% and 81%, to 90% and 86% for the single-label, pair-label, and multi-label models, respectively (all p = 0.01). Prediction accuracy improved over time, significantly reducing the manual correction time. Our active learning and multi-label strategies facilitated accurate automatic segmentation.
Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval: This study was approved by the Institutional Review Board of the Asan Medical Center (IRB No. 2024 − 0485) and Seoul National University Hospital (IRB no. ERI20022) and performed according to the principles of the Declaration of Helsinki. It was based on a review of retrospective charts of patients at Kooalldam Dental Hospital between 2017 and 2020 and at Seoul National University Dental University Hospital between 2010 and 2017. The IRB Committee waived the requirement for informed consent from both institutions, which approved the study due to retrospective observational studies. We received anonymized head and neck CBCT images of patients who don’t have severe maxillofacial deformities, edentulism, or multiple missing teeth with 0.3 mm slice thickness.
(© 2025. The Author(s).)