# Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set
## 1 Introduction  
Three dimensional (3D) segmentation of bones from Cone Beam Computed Tomography (CBCT) is very important for the orthodontics, endodontics, oral and maxillofacial surgeons. Accuracy of segmentation ensures a correct diagnosis, an accurate 3D Virtual Surgical Plan (VSP) and a successful postoperative follow-up of the patients with Cranio-maxillofacial (CMF) deformities [1,2]. Application of CBCT imaging modality in oral and maxillofacial surgery is increased due to its lower radiation dose and shorter acquisition time compared with the conventional multislice CT(MSCT). However, images of CBCT most often are noisier and have beam hardening artifacts. Properties of CBCT also affect the ability to correctly display Hounsfield units (HU) of head tissues (immediately after surgery when edema of soft tissues is present) as opposed to traditional CT [3, 4]. These disadvantages also influence image quality and accuracy of bone segmentation [5]. Due to these reasons, 3D VSP and evaluation of postoperative follow-up must be performed using segmentation by highly experienced experts. Such a procedure of bone segmentation becomes time-consuming [6]. [PERSON] et al. published three studies dedicated to fully automatic bone segmentation using CBCT images [6, 7, 8]. In 2013 [6] and 2014 [7] studies, the principle of the automatic method was a patch-based sparse representation. Patient-specific atlas (probability map) using a sparse label fusion strategy from conventional CT atlases was used as a first estimation. Then a convex segmentation framework was used in order to get the final result. The introduced method led to an accurate segmentation but the basic limitation of this method was computation time (about 5 h [7]). Also, the variety of CBCT data sets (patients with metallic implants, metallic plates and different face deformities) was low in their studies. Data collection of CT to get more variety of the atlases is complicated in relation to the requirements of bioethics. In a 2016 study [8], the same authors also suggested a new automatic method that used the random forest. The multiclass classifier was used to create probability maps for each region of interest (mandible, maxilla and background). The results of the method were achieved almost the same as in the previous study [7] but also with some similar limitations: the limited amount of CBCT data and also computation time of segmentation (20 min). [PERSON] and [PERSON] presented a fully automatic method for mandible segmentation. Segmentation was based on the idea of using a statistical shape model (SSM). They showed accurate results, however, they also--as did the previous authors--tested the algorithm with just six CBCT data sets [9]. [PERSON] et al. [10] proposed an automatic method for mandible segmentation. Marker-based watershed transform method was used in their study. The authors performed accurate and fast enough (12-14 min per data set) segmentation on 20 CBCT data sets. The errors of segmentation were obtained mostly in these three basic areas--around the wisdom teeth, condyles and dental enamel. The reasons of segmentation errors were the different (bigger) or the same intensity of selected basic markers (mandible and background). Performance of manual editing in these areas was recommended. [PERSON] et al. [11] carried out a review of literature on different methods for bone segmentation. The authors discovered that global thresholding is the most used method for bone segmentation. However, a limitation of this method is that the manual post-processing is required. This study presents a fully automatic 3D thresholding method which is based on local statistical information. In the previous pilot study [12], the automatic segmentation of ten CBCT data sets of preoperative patients was performed. We increased the amount and variety (postoperative cases) of data sets (n = 40) in the current study. Compared with the previous study, we included more anatomical areas of interest in order to better evaluate the accuracy of the presented method.