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Abstract

Automatic segmentation of mandibular cortical bone is challenging due to the appearance of teeth that have similar intensity with the bone tissue and the variety of bone intensity. In this paper we propose a new method for automatic segmentation of mandibular cortical bone on cone-beam computed tomography (CBCT) images. The bone tissue is segmented by using Gaussian mixture model for histogram thresholding. The mandibular inferior cortical bone is obtained by incorporating several polynomial models to fit the structure of cortical bone on coronal slices. The buccal and lingual cortical plate is separated by using histogram thresholding for teeth elimination and polynomial fitting for shape extraction. After performing 3D reconstruction, the volumetric cortical bone is obtained. The proposed method gives average accuracy, sensitivity, and specificity value of 96.82%, 85.96%, 97.60%, respectively. This shows that the proposed method is promising for automatic and accurate segmentation of mandibular cortical bone on CBCT images.

In planning a mandibular posterior dental implant, identifying the exact location of the alveolar bone (AB) and mandibular canal (MC) is essential to determine the height and width of the available bone. Cone beam computed tomography (CBCT) is a 3D imaging modality widely used for dental implant planning, which requires a lower radiation dose compared to medical CT and can provide cross-sectional image quality to visualize AB and MC. The radiologist carried out the AB and MC detection processes manually on each section of the CBCT image until the appropriate area was determined for bone measurement. This process is time consuming, and the measurement accuracy depends on the ability and experience of the radiologist. This study proposes an automatic and simultaneous detection system for AB and MC based on 2D grayscale CBCT images, that can simplify and expedite dental implant planning. We introduce Dental-YOLO, an efficient version of YOLOv4 specifically developed to detect AB and MC, with two-scale feature maps at low and high scales. The height and width of the available bone in the implant area were estimated by using the detected bounding box attributes. The AB and MC detection performances using Dental-YOLO reached a mean average precision of 99.46%. The two-way analysis of variance (ANOVA) test showed no difference in the bone height and width measurements produced by the proposed approach and manual measurement by radiologists. Our results suggest that the Dental-YOLO detection system could be helpful for dental implant surgery and presurgical treatment planning.INDEX TERMS Alveolar bone, CBCT, bone measurement, dental implant planning, mandibular canal, object detection, YOLO.