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Background and Objectives <p>With the increased reliance on using CBCT scans for dental implant procedures, the need for accurate and efficient segmentation of mandibular anatomical structures has intensified, posing a significant burden on dentomaxillofacial radiologists. This study addresses the clinical applicability of automated deep learning-based segmentation by comparing three advanced models and proposing a solution.</p> Materials and Methods <p>This study evaluated the performance of three state-of-the-art segmentation models (YOLOv8-seg, nnUNet, and SwinUNETR) on cross-sectional CBCT images for segmenting alveolar bone and inferior alveolar canal. YOLOv8-seg, a single-stage CNN detector with segmentation capacities, was trained on a curated dataset and benchmarked against the other models using standard metrics.</p> Results <p>The YOLOv8-seg model achieved superior segmentation accuracy, with a DSC of 0.962, an IoU of 0.929, and a mean average precision (mAP50) of 0.952. Its inference time (0.00586 sec/image) makes it over 100 times more efficient than the conventional models. Despite some false-positive canal segmentations in the anterior regions, YOLOv8-seg demonstrated strong generalization and clinical promise.</p> Conclusion <p>With further validation and dataset refinement, YOLOv8-seg demonstrates potential as a clinically applicable tool for CBCT image segmentation, offering high-accuracy parameters and significant computational efficiency. Its integration into real-world dental implant planning workflows may reduce clinician workload and improve consistency in decision-making.</p>

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Clinically applicable deep learning model for segmentation of the mandibular bone and inferior alveolar canal in CBCT cross-sectional images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月14日

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  • Mardin Rashid,
  • Shanaz Gaghor ,
  • Ranjdar Talabani

摘要

Background and Objectives

With the increased reliance on using CBCT scans for dental implant procedures, the need for accurate and efficient segmentation of mandibular anatomical structures has intensified, posing a significant burden on dentomaxillofacial radiologists. This study addresses the clinical applicability of automated deep learning-based segmentation by comparing three advanced models and proposing a solution.

Materials and Methods

This study evaluated the performance of three state-of-the-art segmentation models (YOLOv8-seg, nnUNet, and SwinUNETR) on cross-sectional CBCT images for segmenting alveolar bone and inferior alveolar canal. YOLOv8-seg, a single-stage CNN detector with segmentation capacities, was trained on a curated dataset and benchmarked against the other models using standard metrics.

Results

The YOLOv8-seg model achieved superior segmentation accuracy, with a DSC of 0.962, an IoU of 0.929, and a mean average precision (mAP50) of 0.952. Its inference time (0.00586 sec/image) makes it over 100 times more efficient than the conventional models. Despite some false-positive canal segmentations in the anterior regions, YOLOv8-seg demonstrated strong generalization and clinical promise.

Conclusion

With further validation and dataset refinement, YOLOv8-seg demonstrates potential as a clinically applicable tool for CBCT image segmentation, offering high-accuracy parameters and significant computational efficiency. Its integration into real-world dental implant planning workflows may reduce clinician workload and improve consistency in decision-making.