## 6 Conclusions  
This paper aimed at developing a CNN capable of automatically and accurately identifying the IAC, starting from an analysis based on CBCT data. To achieve this goal, a new densely annotated 3D dataset has been collected for the first time. The annotations were performed in terms of voxels, thus guaranteeing 3D data and not virtual reconstructions of the IAC. This preliminary study allowed to improve significantly the manual work of the radiologist/radiology technician. Therefore, the facilitation obtained can be the first step towards the subsequent implementation of the software, with the aim of obtaining a substantial integration between the contribution of the neural network and the human labor. In fact, in the context of modern surgery, obtaining 3D data regarding the anatomy of specific noble structures (e.g., the IAC) is increasingly fundamental. This work sets a new full stop in the segmentation of the inferior alveolar canal on CBCT volumes, recording on of the highest Dice scores currently existing. To summarize, we have demonstrated the following experimental results:  
* EXP1A-B: a dataset with dense annotations is more accurate than a dataset which uses a circular expansion technique only;
* EXP2: the circle expansion technique can have limited results;
* EXP3A-B: a sparsely annotated dataset implemented with circle expansion technique can be helpful to pre-train a CNN. The algorithm was further improved thanks to our innovative deep label propagation method applied to the secondary dataset, to enhance the pre-training of the CNN. This crucial step allowed to achieve the best-ever Dice score recorded in the segmentation of the IAC. The results were also obtained with a considerably lower number of CBCT volumes compared to previously published papers in this field. In addition, for the first time it was provided the free access to the volumes and source code used, so as to be able to work more and more towards the implementation of deep learning in surgical programming. They can be found upon registration at [[https://ditto.ing.unimore.it/maxillo/](https://ditto.ing.unimore.it/maxillo/)]([https://ditto.ing.unimore.it/maxillo/](https://ditto.ing.unimore.it/maxillo/)) (accessed on 28 February 2023). To the best of our knowledge, this is the first public maxillofacial dataset with voxel-level annotations of the Inferior Alveolar Canal. Conceptualization, M.D.B., A.P., F.B., C.G. and A.A.; methodology, S.N., P.M., R.N. and G.C. ; software, L.L., M.C., S.A. and F.P. ; formal analysis, M.D.B., A.P., F.B., L.L., F.P., R.N. and G.C. ; investigation, M.C., S.N., L.C., C.G. and A.A.; resources, P.M., L.C., C.G. and A.A.; writing--original draft preparation, M.D.B., A.P., F.B., M.C., P.M. and R.N. ; writing--review and editing, L.L., S.N., S.A., F.P., G.C., L.C., C.G. and A.A.; visualization, M.C., S.N. and F.P. ; supervision, M.D.B., A.P., F.B., C.G. and A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of University Hospital of Modena (1374/2020/OSS/AOUMO approved on the 10 June 2021). Patients consent was waived due to the retrospective nature of the study and the completely anonymization of the CBCT volumes dataset used. We grant the possibility of free access to both the source code and the datasets used to train and test the CNN. They can be found upon registration at [[https://ditto.ing.unimore.it/maxillo/](https://ditto.ing.unimore.it/maxillo/)]([https://ditto.ing.unimore.it/maxillo/](https://ditto.ing.unimore.it/maxillo/)) (accessed on 28 February 2023). We want to acknowledge [PERSON] for his commendable and precious work. The authors declare no conflict of interest. * [PERSON] (2009) [PERSON]; [PERSON] de Localization of Mandibular Foramen and Clinical Implications. _Int. J. Morphol._**2009**, _27_, 1305-1311. [CrossRef]
* [PERSON] et al.