Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set

Abstract

Due to technical aspects of Cone Beam Computed Tomography (CBCT), the automatic methods for bone segmentation are not widely used in the clinical practice of endodontics, orthodontics, oral and maxillofacial surgery. The aim of this study was to evaluate method’s accuracy for bone segmentation in CBCT data sets. The sliding three dimensional (3D) window, histogram filter and Otsu’s method were used to implement the automatic segmentation. The results of automatic segmentation were compared with the results of segmentation performed by an experienced oral and maxillofacial surgeon. Twenty patients and their forty CBCT data sets were used in this study (20 preoperative and 20 postoperative). Intraclass Correlation Coefficients (ICC) were calculated to prove the reliability of surgeon segmentations. ICC was 0.958 with 95% confidence interval [0.896 ... 0.983] in preoperative data sets and 0.931 with 95% confidence interval [0.836 ... 0.972] in postoperative data sets. Three basic metrics were used in order to evaluate the accuracy of the automatic method—Dice Similarity Coefficient (DSC), Root Mean Square (RMS), Average Distance Error (ADE) of surfaces mismatch and additional metric in order to evaluate computation time of segmentation was used. The mean value of preoperative DSC was 0.921, postoperative—0.911, the mean value of preoperative RMS was 0.559 mm, postoperative—0.647 mm, the ADE value of preoperative cases was 0.043 mm, postoperative—0.057 mm, the mean computational time to perform the segmentation was 46 s. The automatic method showed clinically acceptable accuracy results and thus can be used as a new tool for automatic bone segmentation in CBCT data. It can be applied in oral and maxillofacial surgery for performance of 3D Virtual Surgical Plan (VSP) or for postoperative follow-up.

Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography

“…For multi-slice CT (MSCT), this is usually achieved with thresholding and region growing. In CBCT data however, the intrinsic low image contrast, the lack of Hounsfield Units and increased noise and artifacts make this difficult, requiring substantial manual edits [12,13]. This current semi-automatic (SA) clinical standard is characterized by a high processing time and high risk of user induced error.…”

Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model

“…(2) We propose a PSFE module to extract the spatial mandible information from the mean mandible shape model. (3) The proposed method provides the potential capability of removing the need for post-processing steps, even in cases where the images are corrupted by metal artifacts or noise due to the limitations in their image acquisition.…”

“…Therefore, accurate mandible segmentation plays an important role in 3D VSP for OMFS. Dental CBCT scans are noisier and have more metal artifacts than conventional CTs because dental CBCTs use a low-radiation technique and teeth, dental braces in orthodontic treatment and metal implants in orthognathic treatment are higher attenuation materials, easily leading to high noise and strong metal artifacts in the visual impression of the scans [ 3 ]. The boundaries of mandibles are difficult to be identified since dental braces and metal implants badly affect the image quality in CBCT [ 4 ], as shown in Figure 1 .…”

Special Issue on “Biomaterials for Bone Tissue Engineering”

“…A total of nine papers were published in this area. First, Vaitiekūnas et al [10] presented an automatic method for bone segmentation for the clinical practice of endodontics, orthodontics, and oral and maxillofacial surgery. The automatic method showed clinically acceptable accuracy results versus an experienced oral and maxillofacial surgeon.…”