A triplanar 2D convolutional network ensemble for infra-alveolar nerve segmentation

Cone Beam Computed Tomography scan segmentation is a key step in the digital dentistry workflow. This technology is considered the gold standard for imaging in implant dentistry as well as maxillofacial and oral surgery, where extracting volumetric information is essential. However, manual segmentation is a tedious process that requires significant time and expertise. Therefore, to circumvent these issues, we have developed and present in this study a novel tool for automated nerve segmentation based on deep learning. Specifically, we build on nnU-Net’s self-configuring framework by constructing an ensemble of three 2D convolutional models that leverage information extracted from the three orthogonal planes: axial, coronal and sagittal. We tested our method by participating in the ToothFairy Challenge from the \(26^{th}\) ${26}^{\mathit{th}}$ edition of the International Conference on Medical Image Computing and Computer Assisted Interventions, held in 2023, where we achieved a Dice Similarity Coefficient of 0.66 and a \(95^{th}\) ${95}^{\mathit{th}}$ percentile Hausdorff distance of 8.63.