# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.2 Atlas-Based Methods  
Atlas-based segmentation methods first utilize deformable registration approaches to register a known reference segmentation mask (that form an atlas) to a patient [50]. The registration optimization problem is usually solved by searching the deformation space. Then, this deformation is applied to contours made on the atlas to project the contours back to the patient-space. In 2007, [PERSON] et al. [50] used an atlas-based image segmentation method to automatically segment ROIs in H&N CT images. [PERSON] et al. [45] presented an atlas-based semiautomatic segmentation for the mandible. The authors firstly cropped a minimum enclosing box of the mandible in a raw CT image followed by a user-determined global threshold to remove nonosseous tissue and then used the output from these registrations to transform the 3D mandible template model of the respective template scans into a mandible model that maps to the input test scan. In another paper [62], a semisupervised registration algorithm based on the atlas was developed to accurately segment the OARs with a real ground contour and all other coarse OARs in the atlas. The method concatenates rigid and deformable blocks, takes an atlas image, a group of atlas-space segmentations and a patient image as inputs and outputs the patient-space segmentation of all OARs defined on the atlas. [PERSON] et al. [51] combined atlas registration and organ-specific model-based segmentation in a common framework to segment various organs at risk in H&N CT images. The authors applied atlas registration and organ-specific model-based segmentation at a global level and then used a probabilistic refinement step to refine at the voxel level. [PERSON] et al. [54] applied an atlas-based method for OAR segmentation in H&N and analyzed the dependence of atlas-based automatic segmentation for different levels on anatomy matching between sample patients and atlas patients. In the case of mandible segmentation, the atlas-based method has become increasingly popular, so it has been frequently implemented in commercial or open-source tools. There are several articles using an atlas-based tool, for instance, Advanced Medical Imaging Registration Engine (ADMIRE) v1.05 (Elekta Software) used by [56], Smart Probabilistic Image Contouring Engine (SPICE) used by [57; 58], and PLASTIMATCH MABS provided by [61]. Moreover, [PERSON] et al. [60] quantitatively analyzed three different automatic atlas-based segmentation software offerings for adaptive radiotherapy in a small dataset. In regard to the fusion strategy, [PERSON] et al. [46] built a knowledge base and thresholding technique for nine high-contrast structures (including the mandible) for an online or offline image-guided radiotherapy (RT) application. Then, they further used a hidden Markov model to identify the structures. [PERSON] et al. [49] developed a mandible and maxilla segmentation approach in an H&N CBCB scan that estimated a patient-specific atlas applying a sparse label fusion strategy from predefined CT atlases and then converted it into a convex optimization problem using maximum a posteriori probability for mandible/maxilla segmentation. [PERSON] et al. [52] developed a novel segmentation method that used active contour-joint registration and an atlas selection strategy. A multiple atlas strategy or its modified version is also commonly used because the propagated label cannot be generalized in conventional atlas-based models. To address this issue, multiatlas approaches have been proposed. [PERSON] et al. [47] presented a multiatlas-based method for multiple structure segmentation in CT scans. The authors registered CT images with the atlases at the global level so that structures of interest are aligned approximately in space. Based on that approach, the multiatlas-based segmentation can be performed at the local level. A method presented by [PERSON] et al.