DIR-YOLO: Segmentation of Alveolar Bone and Mandibular Canal in CBCT Images for Dental Implant Recommendation

Naufal, Mohammad Farid and Fatichah, Chastine and Astuti, Eha Renwi and Putra, Ramadhan Hardani (2025) DIR-YOLO: Segmentation of Alveolar Bone and Mandibular Canal in CBCT Images for Dental Implant Recommendation. IEEE Access, 13. pp. 179217-179232. ISSN 2169-3536

PDF DIR-YOLO_Segmentation_of_Alveolar_Bone_and_Mandibular_Canal_in_CBCT_Images_for_Dental_Implant_Recommendation.pdf - Published Version Download (2MB)

Abstract

In dental implant planning, segmentation of the alveolar bone (AB) and mandibular canal (MC) is essential to identify a safe area for dental implant placement in the edentulous molar region. This anatomical information serves as the basis for radiologists to determine the most appropriate dental implant dimension, including its diameter and height. Cone-Beam Computed Tomography (CBCT) is a 3D imaging modality that provides high-resolution cross-sectional images of the jaw. It enables detailed visualization of AB and MC. The dental radiologists manually identify the AB and MC to determine the suitable dental implant dimension. This process is time-consuming and its accuracy depends on the dental radiologist’s skill and experience. This study proposes an automated method for segmenting AB and MC from CBCT images to support dental implant dimension recommendation for molar teeth. This study introduces DIR-YOLO (Dental Implant Recommendation-YOLO), an efficient AB and MC segmentation model based on a modified YOLOv8 architecture. The proposed method enhances YOLOv8 by replacing the original C2f module with C3Ghost, replacing the standard convolution with Ghost Convolution to reduce computational complexity, and simplifying the segmentation head to use only two-scale feature maps. It focuses on low and high resolutions for optimized AB and MC segmentation performance. DIR-YOLO achieved a mean Dice Similarity Coefficient (mDSC) of 92.12% and mean Hausdorff Distance (mHD) of 0.96 mm, indicating high AB and MC segmentation performance. Furthermore, Weighted Cohen’s Kappa and the Kruskal-Wallis test demonstrate a high level of statistical agreement between the proposed method and the dental radiologists and indicating no significant differences in the recommended dental implant dimensions. These results indicate that DIR-YOLO can assist dental radiologists by automating AB and MC segmentation for supporting accurate dental implant dimension recommendation for molar teeth.

Item Type:	Article
Uncontrolled Keywords:	Alveolar bone, mandibular canal, dental implant recommendation, CBCT, segmentation, YOLO
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	MOHAMMAD FARID NAUFAL
Date Deposited:	24 Oct 2025 04:47
Last Modified:	24 Oct 2025 04:47
URI:	http://repository.ubaya.ac.id/id/eprint/49742

Actions (login required)

View Item