## 3 Results
### Transversal vs. Longitudinal Bundles  
Since semantic segmentation allows one to distinguish transversal bundles from longitudinal ones, the quantitative analysis of these two classes of bundles was performed on samples segmented by deep learning. Thus, the transversal and longitudinal collagen bundles, isolated by the neural network, were quantitatively analyzed in four different subvolumes extracted from each sample. This information could not have been obtained by thresholding segmentation, which merely analyzes the image based on grey levels (i.e., on the physical density mismatches) without looking at the morphology of the tissue itself. This quantitative analysis, made possible by semantic segmentation that discriminates transversal from longitudinal bundles, was reported by mean \(\pm\) standard deviation (Table 4) and by box plots (Figure 6). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Volume Percentage**} & \multicolumn{3}{c}{**Anisotropy Degree**} & \multicolumn{3}{c}{**Connectivity Density**} \\  & \multicolumn{2}{c}{**(\%)**} & \multicolumn{2}{c}{**Anisotropy Degree**} & \multicolumn{3}{c}{**(\(\times 10^{-4}\) px\({}^{-3}\))**} \\ \cline{2-6}  & _Transversal_ & _Longitudinal_ & _Transversal_ & _Longitudinal_ & _Transversal_ & _Longitudinal_ \\ \hline Ctr1\_st\_wt & \(28.2\pm 8.9\)* & **11.9\(\pm\) 6.3* & \(0.747\pm 0.014\)* & \(0.619\pm 0.065\)* & \(2.32\pm 0.70\)* & \(0.90\pm 0.60\)* \\ \hline Ctr2\_lt\_no-wt & \(48.0\pm 3.6\)*** & \(5.8\pm 2.2\)*,*** & \(0.787\pm 0.037\)* & \(0.609\pm 0.108\)* & \(2.10\pm 0.49\)** & \(0.30\pm 0.31\)* \\ \hline Ptl\_st\_no-wt & \(45.4\pm 2.5\)*** & \(5.3\pm 1.6\)*** & \(0.834\pm 0.017\) & \(0.747\pm 0.009\) & \(2.77\pm 0.31\)*** & \(0.12\pm 0.11\)*** \\ \hline Pq2\_st\_no-wt\_r & \(38.6\pm 13.0\)* & \(13.8\pm 10.6\)* & \(0.720\pm 0.065\) & \(0.719\pm 0.058\)* & \(1.87\pm 0.98\) & \(1.14\pm 1.34\) \\ \hline Pq2\_st\_no-wt\_l & \(30.2\pm 6.7\) & \(21.6\pm 5.7\)* & \(0.647\pm 0.036\)* & \(0.497\pm 0.065\) & \(1.96\pm 0.58\) & \(4.24\pm 1.52\)* \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Welch _t_-test between morphometric data in transversal vs. longitudinal collagen bundles; Brown–Forsythe and Welch ANOVA test, [PERSON]’s multiple comparisons between samples per parameter (only deep learning data). Test executed by GraphPad Prism 6.0 (GraphPad, Inc., San Diego, CA, USA). a,b,c,d,e: \(p<0.05\); \(\bullet\bullet\): \(p<0.01\); \(\bullet\bullet\bullet\): \(p<0.001\); \(\bullet\bullet\bullet\bullet\): \(p<0.0001\). In terms of volume percentage, the quantity of transverse bundles was always shown to be higher than for longitudinal ones. Figure 6: Box plots of volume percentage, degree of anisotropy and connectivity density calculated on each volume segmented with deep learning: transversal fibers (blue), longitudinal fibers (red). The anisotropy degree was found to be significantly lower for longitudinal bundles than for transversal ones in all of the samples but Pt2_st_no-wt_r. Interestingly, Pt2_st_no-wt_l presented the lowest degree of anisotropy for both longitudinal and transversal bundles. With regard to the connectivity density parameter, almost all samples, with the unique exception of those referred to patient 2, showed significantly higher values in the transversal direction than in the longitudinal one. Notably, the connectivity density of longitudinal bundles in the Pt2_st_no-wt_l sample was found to be considerably larger than in the other samples. In general, referring to Pt2 (i.e., both Pt2_st_no-wt_r and Pt2_st_no-wt_l samples), high standard deviations were found, both considering the volume percentage and the connectivity density; this was not the case with Ctr1, Ctr2 and Pt1 samples. As previously observed, the biopsies extracted from patient 2, in particular the one referring to the left side of the jaw, had very particular characteristics, with high volume percentages and connectivity in the longitudinal direction and low anisotropy degree.