Abstract Background Accurate identification and isolation of the nasopalatine neurovascular bundle are essential for reducing iatrogenic complications during maxillofacial surgical procedures. Manual delineation of this structure on cone beam computed tomography volumes remains challenging because of small anatomical dimensions, interindividual variability, and complex morphology. Automated three dimensional segmentation methods support precise surgical planning, improve intraoperative navigation, and facilitate integration with robotic assisted surgical systems, thereby contributing to improved patient outcomes in high risk maxillofacial interventions. Methods A retrospective analysis was performed on 297 anonymised cone beam computed tomography scans acquired from diagnostic centers and dental clinics across India. DICOM image series and corresponding segmentation masks were converted to NIfTI format to ensure model compatibility. Labeled and unlabeled datasets were curated, followed by extraction of morphometric shape features and clustering based analysis for anatomical characterization. An nnU Net based deep learning model was trained using a three dimensional full resolution configuration with isotropic voxel spacing of 0.15 mm over 750 epochs, incorporating dynamic learning rate scheduling to optimize segmentation performance. Results and Discussion The nnU Net model was trained on 237 volumes and validated on a holdout dataset of 60 volumes. A peak pseudo Dice score of 0.7942 was achieved at epoch 747, with a mean validation Dice score of 0.6879. Segmentation accuracy improved progressively throughout training, demonstrating stable convergence and effective generalization despite the anatomical complexity of the nasopalatine neurovascular bundle. Integration of morphometric feature analysis supported consistent and shape aware segmentation across heterogeneous cone beam computed tomography datasets. Conclusions The proposed nnU Net based framework provides an automated, objective, and scalable solution for segmentation of the nasopalatine neurovascular bundle from cone beam computed tomography volumes. Reduced dependence on manual segmentation and improved anatomical precision support applications in maxillofacial surgery, dental implantology, robotic assisted interventions, and forensic analysis. The findings highlight the potential of deep learning driven segmentation to enhance surgical navigation, reduce neurovascular injury risk, and improve overall clinical workflow efficiency.
Arun et al. (Wed,) studied this question.