# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.5 Deep Learning-Based Methods
For contending with the imbalance between classes mainly caused by small organs, [PERSON] et al. [86] proposed a combined UNet model that consists of 2D UNet, 3D UNet and 3D small UNet. The first model is a 2D UNet model, which has advantages in processing thick slice images. The second model is a 3D UNet model, which can cover most organs with the original resolution in the transverse plane via clipping the scans. The third model is a 3D small UNet model, which focuses on the segmentation of small organs, clipping from the boundary box of the 2D UNet model. [PERSON] et al. [87] proposed a CNN-based autocontouring tool that can be used to detect the errors in autocontours from a clinically validated atlas-based autocontouring tool. They used the DeepMind model [24] to detect the ill-defined contours from an atlas-based autocontouring tool. [PERSON] et al. [24] applied a 3D UNet architecture for OAR segmentation and demonstrated that the 3D UNet approach achieved expert-level performance in delineating multiple organ segmentation in the H&N region. [PERSON] et al. [88] proposed a novel deep learning-based approach based on a generative adversarial network (GAN) with a shape constraint (SC-GAN), where a DenseNet was used to predict the segmentation and a CNN-based discriminator network was applied to correct the predicted errors and image-level inconsistency between the predicted image and ground truth. Moreover, a shape representation loss was applied into the segmentation and adversarial loss function for reducing the false positives. [PERSON] et al. [81] provided a lifelong learning protocol to improve the prediction accuracy of OAR segmentation, in which they used a multitask learning scheme coupled with transfer learning to accomplish this. In this way, the network transfers its shared knowledge to single tasks to help improve the generalizability of the network on limited datasets. To cope with the lack of overall shape and smoothness of OARs, [PERSON] et al. [89] developed the feasibility of learning the signed distance map (SDM) directly from medical scans, in which SDM is usually calculated from object boundary contours and the binary segmentation map. To utilize more information in traditional segmentation training, the authors introduced an approximated Heaviside function to train the model by predicting SDMs and segmentation maps simultaneously. Table 8 lists the summary of literature that used 3D networks for mandible segmentation. However, 3D networks require clipping or resampling the input data into small 3D patches to reduce GPU memory [92]. In addition, integration of 3D patches in post-precessing is a time-consuming workflow. Therefore, to overcome the drawback of 3D networks, using a 2.5D or multiview training strategy instead of 3D is a potential strategy [5; 90]. [PERSON] et al. [5] adopted 2.5D volume of CTs as input in UNet and then combined the resulting 2D segmentations from three orthogonal planes (axial, sagittal and coronal) into a 3D segmentation. This architecture can take into account the spatial information of adjacent slices in order to preserve the connectivity of anatomical structures. [PERSON] et al. [90] first presented a segmental linear function to enhance the intensity of CT images, which makes the organs more separable than the existing simple window width method. Then, the authors proposed a novel 2.5D network for accurate OAR segmentation. Additionally, they applied a novel hardness-aware loss function to make the network give more attention to hard voxels. [PERSON] et al. [91] developed a novel multiview (i.e., axial, coronal, and sagittal view) spatial aggregation framework for joint localization and segmentation of multiple OARs. Additionally, the authors proposed a region-of-interest-based fine-grained representation CNN for predicting the probability maps of OARs for each 2D view of CT images. In this way, the approach unifies the OAR localization and segmentation tasks and trains them in an end-to-end fashion. [PERSON] et al.