# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.3 Level Set-Based Methods
\begin{table}
\begin{tabular}{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{2}{c}{**No. of**} & \multicolumn{4}{c}{**Performance**} & \multicolumn{2}{c}{**Image**} & \multicolumn{2}{c}{**Tina/**} & \multirow{2}{*}{**Category**} \\  & & & **Patients** & & & & & & & & & & \\  & & & **Date (\%)** & **ASD (mm)** & **HD (mm)** & **95 HD (mm)** & **VE (\%)** & **Sen (\%)/PPV (\%)** & **Sen (\%)** & **Sen (\%)/PPV (\%)** & **Sen (\%)** & **Sen (\%)/PPV (\%)** & **Sen (\%)** & **Sen (\%)/PPV (\%)** & **Sen (\%)** \\ \[PERSON] [45] & 2019 & In-house & 54 + 20 & 97.60 \(\pm\) 10.60 & — & — & — & — & — & CT & 3-8 h/CPU & Atlas-based \\ \[PERSON] [46] & 2014 & In-house & 188 & — & — & — & — & — & — & CT & \(-\) & Atlas-based \\ \[PERSON] [47] & 2015 & FDDCA & 40 & 91.70 \(\pm\) 2.34 & — & — & 2.49 \(\pm\) 0.76 & — & — & CT & 100 min/CPU & Atlas-based \\ \[PERSON] [48] & 2008 & In-house & 10 & 90.00 & — & — & — & — & — & CT & 1 h/CPU & Atlas-based \\ \[PERSON] [49] & 2014 & In-house & 13 + 30 & 91.00 \(\pm\) 2.00 & 0.61 \(\pm\) 0.17 & 0.92 \(\pm\) 0.47 & — & — & — & CBCT + CT & — & Atlas-based \\ \[PERSON] [50] & 2007 & In-house & 7 & 80.00 & 0.85 & — & — & — & — & CT & 12 min/CPU & Atlas-based \\ \[PERSON] [51] & 2011 & In-house & 25 & 93.00 & — & 2.64 & — & — & — & CT & 12 min/CPU & Atlas-based \\ \[PERSON] [53] & 2009 & MICCA1 2009 & 18 & 77.80 \(\pm\) 7.40 & — & 16.87 \(\pm\) 6.75 & — & — & — & CT & \(-\) & Atlas-based \\ \[PERSON] [53] & 2009 & MICCA1 2009 & 18 & 93.33 \(\pm\) 1.49 & — & 8.07 \(\pm\) 3.12 & — & — & — & CT & 1 min/CPU & Atlas-based \\ \[PERSON] [54] & 2019 & In-house & 40 & 85.00 (\({}^{1}\)) & — & 9 (\({}^{1}\)) & — & — & CT & — & Atlas-based \\ \hline [PERSON] [50] & 2019 & In-house & 45 & 85.00 (\({}^{1}\)) & — & — & 3.00 (\({}^{1}\)) & 5.00 (\({}^{1}\)) & — & CT & — & Atlas-based \\  & & FDDCA & 32 & 83.00 (\({}^{1}\)) & — & — & 3.00 (\({}^{1}\)) & 52.00 (\({}^{1}\)) & — & CT & — & Atlas-based \\ \[PERSON] [50] & 2016 & In-house & 6 & 89.50 & — & — & — & — & CT & 10 min/— & Atlas-based \\ \[PERSON] [57] & 2013 & In-house & 32 & 89.00 \(\pm\) 4.00 & — & 9.80 \(\pm\) 4.10 & — & — & — & CT & 11.1 min/— & Atlas-based \\ \[PERSON] [58] & 2014 & In-house & 40 & 98.00 \(\pm\) 2.00 & — & — & — & — & CT & 19.7 min/— & Atlas-based \\ \[PERSON] [59] & 2018 & In-house & 128 & 84.00 \(\pm\) 7.00 & 1.89 \(\pm\) 1.55 & 16.83 \(\pm\) 14.90 & — & — & — & CT & 115.9 min/CPU & Atlas-based \\ \hline [PERSON] [46] & 2012 & In-house & 5 & 89.00 \(\pm\) 2.00 & — & — & — & — & \(-\)4.76 \(\pm\) 7.12 & 87.00 \(\pm\) 5.00/ & CT & 10.6 min/CPU & Atlas-based \\ \hline [PERSON] [51] & 2016 & In-house & 25 & 88.00 \(\pm\) 7.00 & — & — & — & — & — & CT & 120 min/CPU & Atlas-based \\ \hline [PERSON] [52] & 2019 & In-house & 500 & 84.50 \(\pm\) 1.60 & — & — & — & — & — & CT & \(-\)/GPU & Atlas-based \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of atlas-based methods. \({}^{(1)}\) Values estimated from figures; actual values not reported.