# Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set
## 4 Discussion  
Automatic methods for bone segmentation are very important in order to get 3D surfaces. It helps surgeons in making correct diagnoses, in performing accurate and quicker VSP and in the evaluation of postoperative follow-up without the influence of surgeon's experience [23, 24, 25, 26, 27]. The aim of this study was to evaluate method's accuracy for bone segmentation in CBCT data sets. The method is based on the histogram filter, 3D sliding window and [PERSON]'s thresholding. The results of automatic segmentation revealed sufficient accuracy of bone segmentation. For the evaluation of method accuracy, three kind of metrics were used: voxel-based-DSC [15, 17, 28], based on the intersurface distance evaluation-RMS and ADE [16, 17]. Additional metric based on the time to perform segmentation-computation time was calculated. The mean DSC coefficient values of two groups (preoperative and postoperative) were bigger than 0.9, which proved the complete overlap and high accuracy of the automatic and surgeon segmented bone voxels. The mean RMS values of intersurface distance for preoperative (0.559 mm) and postoperative (0.647 mm) cases were about two times bigger than the voxel size (0.3 mm). Calculated ADE values showed small discrepancies of surfaces. It shows that segmentation result is good, either we succeeded to avoid superimposition step because both automatic and global segmentations were made by using the same source data sets. In this way, surface superimposition did not yield any additional errors. Compared with the other studies [7, 8, 10], the proposed method performed the segmentation very rapidly ( 46 s/case). The achieved results showed that the proposed automatic method worked accurately.  
However, the proposed method had some limitations. CBCT images were not filtered for metal artefacts (brackets, metal plates and mini-implants) before or after segmentation. Due to this reason, metal artefacts were seen in 3D models. These artefacts hide important areas of bone. Therefore, assessment of the bone near these artefacts became complicated and inaccurate. Especially it is important when postoperative follow-ups are performed [29, 30]. The other limitation of the proposed method is the difficulty to segment areas with low density (thin anatomical areas, e.g., alveolar part of the mandible, mandibular condyles or areas of maxillary sinuses) in CBCT images [31]. This is also valid for other threshold-based segmentation methods. Problematic areas could be segmented including more sensitive techniques [31, 32, 33, 34].  
Further studies may be concentrated to evaluate proposed method with higher amount and different kind of CT/CBCT data sets. The next direction of further study may be to increase accuracy  
Figure 8: Evaluation of two random selected 3D models using different segmentation methods. (**A**) represents 3D models segmented using a global threshold method by the surgeon, (**B**)—segmented with proposed method (region of interest: **1**–condyles, **2**–sinuses).  
of the segmentation for anatomical areas with low density. Fully automatic segmentation of selected anatomical areas especially condyles would be important tool to increase the evaluation of treatment or postoperative follow-up [35; 36].