Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT

Lahoud, Pierre; Diels, Siebe; Niclaes, Liselot; Aelst, Stijn Van; Willems, Holger; Gerven, Adriaan Van; Quirynen, Marc; Jacobs, Reinhilde

doi:10.1016/j.jdent.2021.103891

Cited by 76 publications

(55 citation statements)

References 25 publications

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“…The deep learning approach was initially implemented in dental radiology research [9]. Deep learning has been used to successfully detect bone radiography levels in panoramic radiographs [10], localize the MC on CBCT volume [6], classify teeth on CBCT images [11], segment AB on CBCT images [12], segment the mandibular cortical bone [13], MC [14] [15], tooth [12][16] [17], and inferior alveolar nerve [18] on CBCT images.…”

Section: Introductionmentioning

confidence: 99%

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Dental-YOLO: Alveolar Bone and Mandibular Canal Detection on Cone Beam Computed Tomography Images for Dental Implant Planning

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

“…These results contribute significantly to dental implant planning. U-Net 3D architecture is also used for MC segmentation on AI-driven modules [15]. This study demonstrated a new, fast, and accurate AI-based module for MC segmentation in CBCT.…”

Section: Introductionmentioning

confidence: 99%

Dental-YOLO: Alveolar Bone and Mandibular Canal Detection on Cone Beam Computed Tomography Images for Dental Implant Planning

et al. 2022

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“…The final segmentation evaluation mIoU reached 0.577 at the cost of expense of larger input images, there is still a problem of low accuracy of edge segmentation. Recently, Lahoud et al [15] proposed a novel artificial intelligence driven tool, using using a voxel-wise approach to improve feature acquisition capabilities, the final segmentation evaluation mIoU reached 0.639, but pixel-level annotation is time-consuming. In order to improve the accuracy of mandibular canal image annotation, Cipriano et al [16] proposed a dense voxel-level annotations by reconstructing polygon mesh in the form of the α-shape.…”

Section: Introductionmentioning

confidence: 99%

Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention

Tian

Song

2022

IEEE Access

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In dental implant planning, the mandibular canal is an important reference for determining the safe position of the implant. Accurate and automatic segmentation of the mandibular canal from CBCT image is of great significance. However, the variable curvature of the mandibular canal and the blurred borders make the process challenging. At present, the segmentation of mandibular canal is usually carried out by experienced, doctors using manual or semi-automatic methods, which are time-consuming and have poor segmentation consistency. For these issues, this paper proposes a mandibular canal segmentation method based on 3D convolutional neural network. Firstly, a mandibular canal segmentation method based on center line combined with region growth and a mandibular canal clipping method based on fixed points are proposed to improve the efficiency of mandibular canal marking and reduce the imbalance between sample categories, respectively. Thereafter, the scSE attention module was added to the network for reducing irrelevant background information. Finally, an weighted BCE loss function was used to prevent the mental foramen and mandibular foramen areas from participating in the back-propagation calculation, and the network is more focused on the feature learning of the mandibular canal. The experimental results show that the proposed segmentation method achieves good segmentation results, with a Dice score of 85.9% and a 95% Hausdorff distance of 0.5371mm. Compared with other methods, the segmentation method proposed in this paper had a higher accuracy and a faster efficiency, which is expected to play a practical role in dental implants.INDEX TERMS Deep learning, image processing, intelligent segmentation, oral implantation.

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“…When beginning with the bone pattern assessment, panoramic radiographs are a useful and widely available diagnostic tool ( Pachêco-Pereira et al, 2019 ; Taguchi et al, 1997 ). They allow the identification of bony changes, which are caused by different reasons, including systemic diseases like osteoporosis or diabetes ( Pachêco-Pereira et al, 2019 ), condensing osteitis ( Lahoud et al, 2022 ), and the use of antiresorptive drugs (ARDs), namely bisphosphonates and denosumab ( Moreno-Rabié et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, CNNs have been applied to automatically detect and segment teeth ( Kuwada et al, 2020 ; Leite et al, 2021 ; Vranckx et al, 2020 ) and cystic lesions ( Kwon et al, 2020 ) in panoramic radiographs. Moreover, examples of applications of CNNs in Cone-Beam Computed Tomography (CBCT) include, mandibular canal segmentation ( Lahoud et al, 2022 ) and tooth segmentation and classification ( Shaheen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT

Cited by 76 publications

References 25 publications

Dental-YOLO: Alveolar Bone and Mandibular Canal Detection on Cone Beam Computed Tomography Images for Dental Implant Planning

Dental-YOLO: Alveolar Bone and Mandibular Canal Detection on Cone Beam Computed Tomography Images for Dental Implant Planning

Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention

Convolutional neural network for automated classification of osteonecrosis and related mandibular trabecular patterns

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