## 4 Results  
### Binary Segmentation  
To evaluate the first stage of this workflow, the developed U-Net was compared with the benchmark SegNet. To identify the most effective training strategies for the task, networks trained with various approaches on the same test subset were evaluated, using four volumes. The networks under evaluation included six variations: U-Net with axial training, MPT, and MV and SegNet with axial training, MPT, and MV training. Each network was tested individually using axial slices, sagittal slices, and frontal slices. For majority voting, three separate training sessions were conducted for axial, sagittal, and frontal orientations and then combining the results using a majority voting scheme. This comprehensive evaluation allowed us to rigorously compare the different training strategies and architectures, providing insight into the most effective methods for CBCT segmentation of bones.  
In Figure 4, segmented images of the human foot are presented, featuring one axial, one sagittal, and one frontal view, alongside binary masks obtained from the six networks under evaluation. The proposed networks performed well, with challenges emerging mainly in segmenting frontal and sagittal views using axial-trained networks.  
Figure 4: The figure shows the qualitative results of the six evaluated networks over the anatomical preparation of a human foot.  
The quantitative evaluation of segmentation performance on experimental CBCT images utilized two metrics: the Jaccard index and Dice coefficient. These metrics were computed separately for each volume, and then, the mean and standard deviation of the results across the three test volumes were computed. Table 1 displays the results in terms of the JI and DC. The MPT networks exhibited the highest metrics (\(JI=0.939\pm 0.010\), \(DC=0.969\pm 0.005\)), while _axial_ training during sagittal and frontal tests yielded the lowest metrics (\(JI=0.861\pm 0.014\), \(DC=0.899\pm 0.003\)). In terms of training and segmentation time, MPT requires more time than _axial_ training, while MV requires three times the time requested for _axial_ training due to the need to train three distinct networks to perform the voting scheme. _Axial_ training took nearly 12 h, MPT 24 h, and MV training, involving three networks for the voting scheme, took almost 36 h. The segmentation of a volume (\(512\times 512\times 512\) pixels) took 70 s for the _axial_ and MPT and 220 s for the MV training. Although _majority voting_ training achieved results as good as MPT, it required more computational time, as three predictions have to be made to complete the majority voting scheme.  
The results validated the proposed U-Net trained using MPT's superior performance in handling complicated anatomical structures, underscoring its practical utility in extremity binary segmentation. It is worth noting that SegNet also provides good results in terms of the evaluated metrics, but with a significantly higher number of parameters. This indicates that, while both architectures are effective, U-Net, particularly with MPT, offers a more computationally efficient solution without compromising the quantitative or quality results.