# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.5 Deep Learning-Based Methods  
In recent years, deep learning-based segmentation algorithms have become popular among researchers. With the development of deep learning technology, deep learning methods have shown a tremendous performance in the area of image segmentation. Deep learning techniques provide more flexibility and powerful capabilities than the traditional machine learning methods and require less expert analysis, facilitating extension to the other segmentation tasks [115]. For instance, the popular deep learning architectures, including UNet [13], SegNet [12], etc., are widely used for automatic image segmentation. Consequently, the deep learning approach has shown promising applications in the problem of mandible segmentation. Over the years, many studies have provided better solutions using deep learning architectures than other classical image segmentation methods, as shown in Tables 7-11. [PERSON] et al. [77] proposed the first attempt of using the CNN-based concept to segment OARs in H&N CT scans. Furthermore, the authors smoothed the obtained segmentation results through a Markov random fields algorithm. To address the challenge of low performance on small low-contrast structures in CT, a new loss function, namely, the batch soft Dice loss function, was developed by [PERSON] et al. [78]. The authors used the new loss function in UNet and achieved better results than the methods using other loss functions. [PERSON] et al. [79] developed a symmetric convolutional neural network to force convolution and deconvolution computation to be symmetric. Table 7 shows the summary of using deep learning-based methods. Another intuitive method of 3D segmentation is to train a 3D network to process volume data directly. In this way, the 3D segmentation network can extract 3D structure information. AnatomyNet [82] is built in the 3D U-net architecture using residual blocks in encoding layers. Moreover, a new loss function combining the Dice score and focal loss is applied in the training process. [PERSON] et al. [83] presented a methodology to automatically measure the masseter thickness and locate the ideal injection point for botulinum toxin into the masseter from a CT scan, in which a 3D UNet with a Resblock is used for the mandible and masseter segmentation. [PERSON] et al. [84] applied the 3D segmentation network from [116]. The method consists of a 3D CNN and fully connected 3D CRF. [PERSON] et al. [85] introduced multiscale patches as input for representing the center voxel and designed a 3D CNN network for segmentation of OARs. In addition, the authors interleaved the CNNs designated for the individual tissues since the neighboring tissues are often related on the anatomical side. With this strategy, the patch segmentation result \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{\begin{tabular}{c} **No. of** \\ **Patients** \\ \end{tabular} } & \multicolumn{4}{c}{**Performance**} & \multirow{2}{*}{\
\begin{tabular}{c} **Image** \\ **ASD (mm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Time/** \\ **HD (mm)** \\ \end{tabular} } & \multirow{2}{*}{**Category**} \\ \cline{1-1} \cline{6-10}  & & & & & & & & & & \\ \hline [PERSON] [77] & 2015 & In-house & 50 & \(89.50\pm 3.60\) & — & — & — & CT & 4 min/GPU & DL \\ \hline [PERSON] [78] & 2019 & PDDCA & 35 & \(94.60\pm 0.70\) & \(0.29\pm 0.03\) & — & — & CT & — & DL \\ \hline [PERSON] [79] & 2018 & In-house & 93 & \(90.76\pm 2.45\) & — & — & \(85.44\pm 3.99\) & CT & —/GPU & DL \\ \hline [PERSON] [80] & 2021 & PDDCA & 48 & \(94.00\pm 2.00\) & \(0.49\pm 0.18\) & \(2.36\pm 0.62\) & — & CT & —/GPU & DL \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of deep learning-based methods. of a specific tissue can be refined by the other neighboring tissues. This study can deal with large tissues, while the small tissues are still not that promising in terms of Dice and 95 HD.