## 5 Discussion
In fact, the CNN was trained at the same time on a dataset implemented by the circular expansion technique and on a dataset with voxel-based annotations. Therefore, we sought to maximize the primary and secondary datasets in experiment 3B to overcome this limit. In particular, using the secondary dataset with sparse annotations and the circular expansion method, we effectively pre-trained the CNN. The use of pre-training has been shown to perform a role in improving CNNs performance [57]. Indeed, the pre-trained CNN was then properly trained with the primary dataset. The IoU score obtained was 0.54 and the Dice score was 0.69. Regarding both parameters, these were certainly the best overall results obtained. The main difference between the experiments 1B and 3B consisted of the pre-training performed on the secondary dataset, which could have performed a key-role in providing the CNN a raw understanding of the IAC anatomy. This uptake was then refined with the primary dataset. Therefore, these results demonstrated the usefulness of the circular expansion technique mainly in the pre-training phase of developing a CNN. The aim of this paper was to create a reliable CNN to three-dimensionally identify the mandibular canal and provide a public voxel-level annotations dataset of the Inferior Alveolar Canal. However, some considerations about the numerical results obtained are mandatory. Comparing our results to the previously published ones, two main papers are to be considered [12; 51]. When the results of this paper were collected, the previous best results were obtained by [PERSON] et al. based on 637 CBCT volumes sparsely annotated. Using a circular expansion technique, they obtained a Dice score value of 0.58. It has to be noted that the data produced in this experiment are bidimensional. In the experiment 3B, we were able to overcome this amazing result, achieving a Dice score of 0.69 and an IoU score of 0.54. As explained, this was achieved by pre-training the CNN on a sparse annotated dataset and applying a circular expansion technique; subsequently, a voxel-level annotated dataset of the IAC was employed. Thus, our findings are superior both in terms of accuracy and amount of data compared to [PERSON]'s, since the IAC is annotated on a 3D-base. In a similar way, [PERSON] et al. used 3D annotations of the IAC, obtaining a Dice score of 0.77 and an IoU score of 0.64 [12]. Similarly to our technique, [PERSON] et al. also centered the annotation protocol on a three-dimensional basis. Nonetheless, this result was acquired using a much wider densely annotated dataset (235 CBCT volumes with 3D annotations) compared with the present study (91 CBCT volumes densely annotated). Interestingly, in both cases (the present paper and [PERSON]'s one), the number of volumes needed to overcome [PERSON]'s results was significantly lower: [PERSON] needed 637 CBCT volumes compared to [PERSON]'s 235 and our 91 CBCT volumes [12; 51]. These findings prove that the voxel-based annotation technique is much more informative and efficient than the circular expansion one. Finally, we committed ourselves to overcome the result presented by [PERSON] et al., who recorded a Dice score of 0.77 and an IoU score of 0.64 [12]. So, a new expansion technique of the sparsely-annotated dataset was added. In fact, we were able to transform the 2D-annotations of the IAC into 3D-voxel level information and we also introduced the positional concept in our algorithm (as explained in the subsection \"4.3.2. Optimization of the algorithm\"). We called this novel method \"deep label propagation method\" and further technical insights were described in a previous paper [58]. A Dice score of 0.79 and IoU score of 0.64 were recorded. While this achievement has an enormous room for improvement, it has to be said that the Dice score threshold of 0.75 is commonly considered an acceptable score for the use in clinical practice [12].