# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.4 Classical Machine Learning-Based Methods  
In the past few decades, the role of machine learning in medical applications has greatly increased. Classical machine learning (CML) methods include thresholding techniques, linear regression, support vector machines, random forests, etc. Many studies have used CML methods to segment the mandible, as listed in Table 6.  
Normally, this technique is implemented in a hybrid way. A multiple threshold method by [PERSON] [114] was used to calculate proper thresholding values in the segmentation phase in the article from [PERSON] et al. [67]. After thresholding, region growing was applied so that many bone structures could be segmented, such as parts of the jaw bone or spine. The largest connected component of the segmented image volume can be simply selected for mandible segmentation [67]. Later, [PERSON] et al. [65] made use of random forests to segment the mandible and maxilla in their expended CBCT database and achieved better results than their previous method [9]. [PERSON] et al. [19] proposed a semiautomatic two-stage segmentation method for CBCT. The authors first performed bone segmentation using a supervoxel technique and then implemented graph clustering with an interactive user-placed seed process that was used for mandible and skull segmentation. In this way, supervoxel methods can reduce the excessive number of voxels of CBCT 3D volume, leading to shorter processing time. [PERSON] et al. [68] introduced a new patch-based label fusion approach to weight the label votes using a generative probabilistic approach, in which local classifiers from the dictionary of atlas patches were generated for weighting the probability of the target. [PERSON] et al. [69] integrated shape priors with a hierarchical learning model, in which they introduced 3 novel strategies: hierarchical critical model vertex identification, joint learning of shape and appearance, and hierarchical vertex regression. [PERSON] et al. [70] applied point-based registration and model-based segmentation and introduced a novel voxel classification approach to improve the results from model-based segmentation. [PERSON] et al. [71] adopted random forest regression to detect the mandible in 3D and then utilized a 3D gradient-based fuzzy connectedness image segmentation algorithm for final segmentation. [PERSON] et al. [72] built population fuzzy anatomy models to find the hierarchical relationship of the objects. Then, the authors further utilized the anatomical information in the hierarchical relationship to locate the objects. Finally, the segmentation could be generated according to the location results. [PERSON] et al. [36] presented support vector regression to predict all the boundary points of the organs of interest. [PERSON] et al. [75] performed histogram equalization and grayscale morphology for mandible segmentation. Then, image binarization was performed after grayscale method. Finally, 3D template matching was performed using cross-correlation to find a point inside the mandible followed by a geodesic dilation using the binary image as mask. [PERSON] et al. [73] introduced new features into the automatic anatomy recognition approach from [66] in which they combined texture and intensity information into the recognition procedure. [PERSON] et al. [74] developed a methodology, called AAR-RT, which extended their previous AAR framework [66] to RT planning in H&N. [PERSON] et al. [76] applied a fully automatic mandible segmentation method to support orthodontists in assessing facial asymmetry, in which they used morphological operations for extraction of the mandible, and then, the connected components were computed and considered part of the mandible in each 2D slice.