# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.5 Deep Learning-Based Methods
[92] integrated a recurrent unit and a vanilla network, which enable the network to learn the continuity of neighborhood slices for the scans in the 2D architecture. Table 9 shows the summary of using 2.5D networks for mandible segmentation. \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{2}{c}{**No. of**} & \multicolumn{4}{c}{**Performance**} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Time/**} & \multirow{2}{*}{**Category**} \\ \cline{5-16}  & & & & **Dice (\%)** & & & & & & & & & & \\ \hline [PERSON] [81] & 2019 & In-house & 200 & 91.00 \(\pm\) 9.00 & — & — & — & 0.66\(\pm\) 0.31 & — & CT & 20 s/GPU & DL \\ \[PERSON] [82] & 2019 & PDCCA + TCIA & 48 \(\pm\) 223 & 92.30 & — & — & — & — & — & CT & 0.12 s/GPU & DL \\ \[PERSON] [53] & 2019 & In-house & 53 & 94.60 \(\pm\) 1.10 & 0.24 \(\pm\) 0.024 & — & — & 95.60 \(\pm\) 1.20 & — & — & CT & 1 s/GPU & DL \\ \[PERSON] [54] & 2018 & In-house & 70 & 95.90 & 0.60 & 6.48 & — & — & — & — & CT & 1 min/GPU & DL \\ \[PERSON] [56] & 2018 & PDCCA & 48 & 92.00 & — & — & 1.89 & — & — & — & CT & —/GPU & DL \\ \[PERSON] [56] & 2020 & StructSeg2019 & 50 & 90.30 (\(L\)) & — & — & — & — & — & — & CT & 1 min/GPU & DL \\ \[PERSON] [87] & 2019 & In-house + TCIA & 1403 \(\pm\) 24 & 86.80 \(\pm\) 3.30 & — & 12.80 \(\pm\) 9.50 & — & — & — & — & CT & 2 min/GPU & DL \\ \[PERSON] [24] & 2018 & In-house & 459 & 93.10 \(\pm\) 1.90 & — & — & — & — & — & — & CT & \(\rightarrow\)/GPU & DL \\ \hline  & & & & & & & & & & & & & \\ \hline  & & & & & & & & & & & & & \\ \[PERSON] [88] & 2019 & PDCCA & 32 & 99.51 \(\pm\) 1.30 & 0.55 \(\pm\) 0.14 & — & 2.09 \(\pm\) 0.63 & 91.25 \(\pm\) 2.70 & — & 96.82 \(\pm\) 1.70 & CT & 14 s/GPU & DL \\ \hline  & & & & & & & & & & & & \\ \hline [PERSON] [89] & 2019 & PDCCA & 32 & 99.51 \(\pm\) 1.30 & 0.55 \(\pm\) 0.14 & — & 2.72 \(\pm\) 1.31 & 86.65 \(\pm\) 6.00 & — & 78.50 \(\pm\) 4.30 & MRI & 14 s/GPU & DL \\ \hline  & & & & & & & & & & & \\ \hline  & & & & & & & & & & & \\ \hline  & & & & & & & & & & & & \\ \hline  & & & & & & & & & & & & \\ \hline  & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 8: Summary of 3D network strategies of deep learning-based methods. \begin{table}
\begin{tabular}{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**} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Time/**} & \multirow{2}{*}{**Category**} \\ \cline{5-16}  & & & & **Dice (\%)** & & & & & & & \\ \hline Qui [5] & 2019 & In-house & 109 & 88.10 & — & — & 0.58 & CT & 2.5 min/GPU & DL \\ \multicolumn{1}{c}{PDCCA} & 40 & 93.28 \(\pm\) 1.44 & — & — & 1.43 \(\pm\) 0.56 & — & — & & \\ \hline Lei [90] & 2021 & StructSeg2019 & 50 & 91.10 \(\pm\) 2.90 (\(L\)) 1.979 \(\pm\) 1.50 (\(R\)) & & 2.81 \(\pm\) 0.45 (\(L\)) 2.70 \(\pm\) 0.40 (\(R\)) & —The attention model has been widely used in various fields of deep learning in recent years [117; 118; 119]. The attention mechanism focuses limited attention on key information to save resources and obtain the most effective information quickly [118]. [PERSON] et al. [93] designed a self-channel-and-spatial-attention neural network for H&N OAR segmentation, in which spatial and channelwise attention learning mechanisms can adaptively force the network to emphasize the meaningful features and weaken the irrelevant features at the same time. A novel simple local blockwise self-attention-based segmentation approach was presented by [PERSON] et al. [94]. This approach can more easily make the network learn the spatial location and interrelation within input images. This study demonstrated that adding the additional attention blocks increases the contextual field and captures focused attention from anatomical structures. [PERSON] et al.