# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.4 Classical Machine Learning-Based Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**No. of Patients**} & \multicolumn{3}{c}{**Performance**} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Time/**} & \multirow{2}{*}{**Category**} \\ \cline{5-5} \cline{7-10}  & & & & & & & & \\ \hline [PERSON] [9] & 2014 & In-house & 15 & \(92.00\pm 2.00\) & \(0.65\pm 0.19\) & \(0.96\pm 0.53\) & CBCT & 5 h/CPU & Level set-based \\ \hline [PERSON] [63] & 2009 & MICCAI 2009 & 18 & \(87.93\pm 2.06\) & — & \(8.70\pm 3.28\) & CT & — & Level set-based \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of level set-based methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**No. of**} & \multicolumn{4}{c}{**Performance**} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Time/**} & \multirow{2}{*}{**Category**} \\  & & & & & & & & & & & & & \\  & & **Patients** & & & & & & & & & & & \\ \hline [PERSON] [64] & 2013 & In-house & 12 & 97.90 \(\pm\) 1.10 & 0.20 \(\pm\) 0.13 & — & 95.80 \(\pm\) 2.00 & — & — & MRI & 128 \(\mu\)s/CPU & CML \\ \hline [PERSON] [65] & 2015 & In-house & 30 & 94.00 \(\pm\) 2.00 & 0.42 \(\pm\) 0.15 & 0.74 \(\pm\) 0.25 & — & — & — & & \multicolumn{1}{c}{CCT} & 20 min/\(--\) & CML \\  & & 60 & 95.00 \(\pm\) 2.00 & 0.33 \(\pm\) 0.11 & 0.41 \(\pm\) 0.20 & — & — & — & & \multicolumn{1}{c}{CT} & & \\ \[PERSON] [66] & 2014 & In-house & 15 & — & — & 3.30 \(\pm\) 0.56 & — & 1.00 \(\pm\) 0.00 & — & MRI & 54 \(\mu\)s/CPU & CML \\ \[PERSON] [19] & 2019 & In-house & 16 & 92.88 & — & — & 86.48 & — & — & \multicolumn{1}{c}{CBT} & 5 min/CPU & CML \\ \[PERSON] [67] & 2009 & In-house & 12 & — & — & — & — & — & \multicolumn{1}{c}{CT} & 10 \(\mu\)s/CPU & CML \\ \[PERSON] [68] & 2015 & PDCDA & 40 & 93.68 \(\pm\) 2.36 & — & — & — & — & \multicolumn{1}{c}{CT} & \(--\) & CML \\ \[PERSON] [69] & 2016 & PDCDA & 48 & 94.40 \(\pm\) 1.30 & 0.43 \(\pm\) 0.12 & — & — & — & \multicolumn{1}{c}{CT} & \(--\)/CPU & CML \\ \[PERSON] [70] & 2010 & In-house & 25 & 90.19 & — & — & — & — & \multicolumn{1}{c}{CT} & 3 min/CPU & CML \\ \[PERSON] [71] & 2017 & PDCDA & 40 & 91.00 & — & \(<\)1.00 & — & — & \multicolumn{1}{c}{CT} & \(--\)/CPU & CML \\ \[PERSON] [72] & 2018 & In-house & 216 & 89.00 & — & 1.60 & — & — & \multicolumn{1}{c}{CT} & \(--\) & CML \\ \[PERSON] [76] & 2018 & In-house & 56 & 85.20 \(\pm\) 5.30 & — & — & — & \multicolumn{1}{c}{—} & 0.10/3.16 & \multicolumn{1}{c}{CT} & 1 \(\mu\)s/CPU & CML \\ \[PERSON] [73] & 2018 & In-house & 246 & — & — & — & — & — & \multicolumn{1}{c}{CT} & \(--\) & CML \\ \[PERSON] [74] & 2019 & In-house & 216 & 89.00 & — & — & — & — & \multicolumn{1}{c}{CT} & \(--\) & CML \\ \hline [PERSON] [73] & 2018 & PDCDA & 30 & 80.49 & — & — & — & — & \multicolumn{1}{c}{CT} & \(--\) & CML \\ \hline [PERSON] [76] & 2012 & In-house & 10 & — & — & — & — & \multicolumn{1}{c}{\(--\)} & \(--\) & \multicolumn{1}{c}{CT} & \(--\) & CML \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of classical machine learning-based methods. Along with the quick development of computer and MRI imaging technology, MRI images have also been used in mandible segmentation. [PERSON] et al. [64] presented a two-stage rule-constrained seedless region growing approach for the mandible segmentation in MRI images. A 3D/2D seedless region growing approach is used to detect a trabecular bone and cortical bone of mandible after using a thresholding technique. The mandibular body was finally merged with some morphological processes. [PERSON] et al. [66] developed an automatic anatomy recognition methodology in H&N MRI scans that is based on fuzzy modeling ideas and tightly integrated fuzzy models with the iterative relative fuzzy connectedness delineation algorithm to locate and delineate organs in different body scans.