# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.5 Deep Learning-Based Methods
[95] presented an end-to-end CNN, called AttentionAnatomy, which was trained with three partially annotated datasets to segment OARs from the whole body. AttentionAnatomy retains the basis structure of UNet and has two branches: a CT region classification path and an OAR segmentation path. The CT region classification path outputs region prediction and attention vectors for OARs, which represent the inference of possible combinations of OARs in the current image. Then, the OAR segmentation path applies these attention vectors to modulate the final output mask. The authors further proposed a recalibration mechanism to solve the partial annotation problem. Moreover, a hybrid loss function composed of batch Dice loss and spatially balanced focal loss is used to address the extreme category imbalance. [PERSON] et al. [96] developed a cross-layer spatial attention map fusion network to combine different spatial attention maps and establish connections for significant features in the feature maps. Furthermore, the authors adopted a top-k exponential logarithmic Dice loss for the imbalanced dataset in their segmentation tasks. Table 10 shows the summary of using attention strategies for mandible segmentation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**No. of**} & \multicolumn{4}{c}{**Performance**} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Time/**} & \multirow{2}{*}{**Category**} \\ \cline{4-4} \cline{8-11}  & & & & & **Dice (\%)** & & & **ASD (mm)** & **95 HID (mm)** & **Re/Sen (\%)** & **PPV (\%)** & **Modalities** & **Equignment** \\ \hline Gou [93] & 2020 & PDPCA & 48 & 94.00 \(\pm\) 1.00 & 0.47 \(\pm\) 0.11 & 1.40 \(\pm\) 0.02 & 93.00 \(\pm\) 2.00 & 95.00 \(\pm\) 2.00 & CT & 2 s/GPU & DL \\ [PERSON] [94] & 2019 & In-house + PDPCA & 48 + 48 &
\begin{tabular}{c} 93.00 \(\pm\) 1.00 \\ (trained in-house dataset) \\ \end{tabular} & — & — & — & — & CT & 0.1 s/GPU & DL \\ \hline [PERSON] [95] & 2020 & In-house & 129 & 94.05 \(\pm\) 1.08 & — & — & — & CT & —/GPU & DL \\ \hline [PERSON] [96] & 2020 & StrucSeg2019 & 50 & 90.28 (\(L\)); 90.81 (\(R\)) & — & — & — & — & CT & —/GPU & DL \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of attention strategies of deep learning-based methods. There are several two-stage training strategies, such as cascaded CNNs [97; 98; 99; 105], the combination [101; 103] of Faster R-CNN [120] and CNNs. [PERSON] et al. [97] developed a cascaded CNN architecture for multitasks segmentation in H&N scans, in which a slice classification network is proposed to classify CT slices into the corresponding target categories. Then, the irrelevant slices without the targets were excluded. Next, the slices in the corresponding categories were pushed to a refined 3D segmentation network for target segmentation. In this case, the prediction performance was further improved with the help of the slice classification network. [PERSON] et al. [98] trained two hierarchical 3D neural networks to segment multiple OARs in H&N CT scans. The authors first implemented a coarse network on size-reduced scans for locating the OARs. Then, a subsequent fine network on the original resolution scans was trained for a final accurate segmentation based on the results from the coarse network. [PERSON] et al. [99] applied two cascade networks for location and fine segmentation of organ segmentation, in which the network is modified based on the squeeze and exception module, the residual module and V-Net [121]. There are still some shortcomings in this research. The network input is accomplished by resampling the input images with the same voxel size. However, the difference of the thickness and the resolution within the slices is often too large. Therefore, the operation of resampling in the first network can easily cause localization failure when organs have small volumes. [PERSON] et al. [100] proposed a framework similar to [99]. [PERSON] et al.