# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 4 Discussion
\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/ Modalities**} & \multirow{2}{*}{**Equipment**} & \multirow{2}{*}{**Category**} \\ \cline{5-6} \cline{8-8}  & & & & & & & & & \\ \hline Zhang [97] & 2021 & In-house & 170 & \(89.00\pm 2.00\) & — & \(1.66\pm 0.51\) & CT & \(40.1\,\mathrm{s}/\mathrm{GPU}\) & DL \\ \hline Tappenier [98] & 2019 & PDDCA & 40 & \(91.00\pm 2.00\) & — & \(2.4\pm 0.6\) & CT & \(38.3\,\mathrm{s}/\mathrm{GPU}\) & DL \\ \hline Mu [99] & 2020 & In-house & 50 & \(\begin{matrix}89.80\pm 2.70\,(L)\); \\ \(90.40\pm 2.00\,(R)\end{matrix}\) & — & — & CT & \(3\,\mathrm{s}/\mathrm{GPU}\) & DL \\ \hline Wang [100] & 2018 & PDDCA & 48 & \(93.00\pm 1.90\) & — & \(1.26\pm 0.50\) & CT & \(6\,\mathrm{s}/\mathrm{GPU}\) & DL \\ \hline \multirow{3}{*}{Tang [32]} & \multirow{3}{*}{2019} & In-house & 175 & \(93.12\pm 1.41\) & \multirow{3}{*}{\(2.48\pm 0.83\)} & \multirow{3}{*}{CT} & \multirow{3}{*}{2\(\,\mathrm{s}/\mathrm{GPU}\)} & \multirow{3}{*}{DL} \\  & & \(35+105\) & \(89.31\pm 11.59\) & — & \(3.05\pm 2.60\) & CT & \(2\,\mathrm{s}/\mathrm{GPU}\) & DL \\ \cline{1-1}  & & PDDCA & 48 & \(95.00\pm 0.80\) & — & — & & \\ \hline Lei [101] & 2020 & In-house & 15 & \(85.00\pm 4.00\) & — & — & MRI & — & DL \\ \hline Lei [102] & 2020 & In-house & 15 & \(88.00\pm 3.00\) & — & — & CT & — & DL \\ \hline Liang [103] & 2019 & In-house & \(185\) & \(\begin{matrix}91.40\pm 0.04\,(L)\); \\ \(91.20\pm 3.00\,(R)\end{matrix}\) & — & — & CT & \(30\,\mathrm{s}/\mathrm{GPU}\) & DL \\ \hline Dijk [104] & 2020 & In-house & 693 & \(94.00\pm 1.00\) & — & \(1.30\pm 0.50\) & CT & — & DL \\ \hline Men [105] & 2019 & HNSCC & 100 & \(92.00\pm 2.00\) & \(2.40\pm 0.40\) & — & CT & \(5.5\,\mathrm{min}/\mathrm{GPU}\) & DL \\ \hline Egger [106] & 2018 & In-house & 20 & \(89.64\pm 1.69\) & — & — & CT & —/CPU & DL \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of two-stage strategies in deep learning-based methods. As shown in Figure 3c, most of the publications (86.4%) used the CT imaging modality. Very few publications worked with CBCT (8.6%) and MRI (4.9%). Moreover, most of the publications worked with only one single imaging modality. There are 4 publications that worked with two imaging modalities. As shown in Figure 3c, number of publications using CT modality is much more than that of using CBCT and MRI techniques, which need more effort for manual expert annotation. However, the CBCT and MRI imaging techniques involve very low-dose radiation or no radiation. Mandible segmentation in CBCT and MRI is worthy of further exploration. The ability to derive the exact shape of the mandible from MRI's nonionizing radiation imaging mode provides clinicians with a value-added visualization option to view hard and soft tissues in a visual environment. Especially when high-resolution bone information is not needed, the spatial relationship between soft tissue muscle and the mandibular body can be obtained without additional volume imaging using CBCT. Patients who receive MR imaging but do not require facial CT imaging can benefit from this approach without the need for CBCT or X-ray radiation from CT imaging. This application eliminates the radiation risk to patients [3]. Figure 3: Analysis of publications pertaining to mandible segmentation. (**a**) The share of methodology categories used in the publications. (**b**) Distribution of the publications by year. (**c**) The share of image methodologies used in the publications. (**d**) Box plot of the distribution of the Dice score of the PDDCA test from the publications. The black lines in the middle of the box indicate the average Dice score. The Head and Neck Auto Segmentation MICCAI Challenge (2015) [29], which used the PDDCA dataset in the challenge, has obtained much attention during the past years.