## 4 Results  
To evaluate the accuracy of the two presented methods, a comparison on 20 volunteers (10 female, 10 male) of various postures was conducted. To check if the proposed method is defect-resistant, in the validation group, there were two scans with significant flaws in the form of holes and five with lesser defects; these are presented in Figure 16. Each scan was processed using both methods.  
Figure 15: Images generated using orthogonal projection (**a**) and mid-surface projection (**b**).  
The machine-learning task was performed on the same neural networks with the use of identical configurations and similar training duration expressed in the number of epochs (about 55,000). To compare results, distances between computed and manually-marked landmarks (considered as ground truth points) were measured. Distances were calculated using the following formula (4):  
\[d=\sqrt{\left(x_{c}-x_{m}\right)^{2}+\left(y_{c}-y_{m}\right)^{2}+\left(z_{c}-z \right)^{2}}, \tag{4}\]  
where \(d\) is distance; \(x_{c},y_{c},z_{c}\) are computed coordinates of the landmark; and \(x_{m},y_{m},z_{m}\) are coordinates of the manually-marked characteristic point. Symmetrical characteristic points were considered as the same landmark. For all types, arithmetic means and standard deviations were calculated. Results are presented in Figure 17 and provided in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Landmark**} & \multicolumn{3}{c}{**Orthogonal Projection**} & \multicolumn{3}{c}{**Mid Surface Projection**} & **Average** & **Standard** \\ \cline{2-9}  & **Average** & **Standard** & **Gross** & **Average** & **Standard** & **Gross** & **Distance** & **Deviation** \\  & **Distance** & **Deviation** & **Error** & **Distance** & **Deviation** & **Error** & **Difference** & **Difference** \\ \hline V & 23.4 & 9.4 & - & 17.3 & 9.8 & - & \(-\)26\% & \(+\)5\% \\ C\({}_{\rm L}\) & 23.9 (16.1) & 15.8 (6.6) & 5\% & 15.1 & 5.8 & - & \(-\)37\% (\(-\)6\%) & \(-\)63\% (\(-\)12\%) \\ N\({}_{\rm SL}\), N\({}_{\rm SR}\) & 27.5 & 7.0 & - & 18.4 & 5.6 & - & \(-\)33\% & \(-\)21\% \\ N\({}_{\rm B}\) & 30.9 (20.2) & 18.4 (6.6) & 15\% & 13.9 & 5.3 & - & \(-\)55\% (\(-\)31\%) & \(-\)71\% (\(-\)20\%) \\ S\({}_{\rm L}\), S\({}_{\rm R}\) & 22.0 & 7.6 & - & 15.8 & 6.2 & - & \(-\)28\% & \(-\)17\% \\ A\({}_{\rm FLR}\), A\({}_{\rm FHR}\) & 38.4 (27.2) & 26.2 (12.8) & 10\% & 21.3 & 8.1 & - & \(-\)44\% (\(-\)21\%) & \(-\)69\% (\(-\)37\%) \\ B\({}_{\rm L}\), B\({}_{\rm R}\) & 23.9 & 12.8 & - & 21.5 & 7.4 & - & \(-\)10\% & \(-\)42\% \\ E\({}_{\rm L}\), E\({}_{\rm R}\) & 32.7 & 10.1 & & 25.5 & 11.3 & - & \(-\)22\% & \(+\)12\% \\ R\({}_{\rm L}\), R\({}_{\rm R}\) & 29.7 & 10.0 & - & 37.5 & 9.7 & - & \(+\)26\% & \(-\)3\% \\ H\({}_{\rm L}\), H\({}_{\rm R}\) & 28.6 & 12.2 & - & 29.2 & 7.6 & - & \(+\)2\% & \(-\)38\% \\ W\({}_{\rm L}\), W\({}_{\rm R}\) & 18.9 & 8.1 & - & 18.0 & 6.8 & - & \(-\)5\% & \(-\)16\% \\ C\({}_{\rm R}\) & 21.7 & 4.9 & - & 29.2 & 3.6 & - & \(+\)35\% & \(-\)27\% \\ C\({}_{\rm KL}\), C\({}_{\rm KR}\) & 29.7 & 10.4 & - & 17.3 & 7.1 & - & \(-\)42\% & \(-\)31\% \\ General & 27.4 (25.5) & 11.7 (9.5) & 1.8\% & 22.0 & 7.5 & - & \(-\)20\% (\(-\)14\%) & \(-\)17\% (\(-\)11\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results expressed in mm. Scores of the orthogonal projection were divided into the raw distances and filtered gross error values (presented in parentheses if applicable).  
Figure 16: Examples of data with different types of defects: lesser (**a**) and significant (**b**).