Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering

Abstract

Cone beam computed tomography (CBCT) is a medical imaging technique employed for diagnosis and treatment of patients with cranio-maxillofacial deformities. CBCT 3D reconstruction and segmentation of bones such as mandible or maxilla are essential procedures in surgical and orthodontic treatments. However, CBCT image processing may be impaired by features such as low contrast, inhomogeneity, noise and artifacts. Besides, values assigned to voxels are relative Hounsfield units unlike traditional computed tomography (CT). Such drawbacks render CBCT segmentation a difficult and time-consuming task, usually performed manually with tools designed for medical image processing. We present an interactive two-stage method for the segmentation of CBCT: (i) we first perform an automatic segmentation of bone structures with super-voxels, allowing a compact graph representation of the 3D data; (ii) next, a user-placed seed process guides a graph partitioning algorithm, splitting the extracted bones into mandible and skull. We have evaluated our segmentation method in three different scenarios and compared the results with ground truth data of the mandible and the skull. Results show that our method produces accurate segmentation and is robust to changes in parameters. We also compared our method with two similar segmentation strategy and showed that it produces more accurate segmentation. Finally, we evaluated our method for CT data of patients with deformed or missing bones and the segmentation was accurate for all data. The segmentation of a typical CBCT takes in average 5 min, which is faster than most techniques currently available.

Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network

“…Providing a fully automated segmentation model requires considerable work, considering the time and effort invested in preparing the training datasets. However, once the trained models are provided, the segmented mandible can be accessed without the tedious and time-consuming processes in manual or semi-automatic segmentation methods [ 28 – 30 ]. On an NVIDIA Geforce RTX 2080 Ti running the two-step prediction pipeline on a DICOM dataset takes approximately 31 s.…”

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR

“…Traditional image processing methods, such as super-voxels and graph clustering [ 19 ], atlas-based segmentation [ 8 , 20 ], watershed methods [ 21 ] are available tools that presented good accuracy for segmentation, however, due to image artifacts and noise, that can be caused by intercuspation of the dentition and the presence of metallic crowns, it is still a challenge to segment the images properly and also to segment different tissues such as bone with different densities (boundaries) and soft tissues. Due to these limitations, machine learning methods for image segmentation in dentistry have become popular, and the major limitation in training AI models such as the proposed AMASS is to have a gold standard to serve as training models [ 22 ].…”

System of gender identification and age estimation from radiography: a review

“…Figure 2. The mandibular segmentation methods: (a) SCNN-11C [18], (b) RCNNSeg [19], (c) fuzzy connectivity [20], (d) U-Net CNN [21] of CT scan, (e) SSM [22], (f) super-voxels and graph clustering [23],…”