Automated Multi-class Segmentation of Mandibular Odontogenic Cysts and the Mandibular Canal on CBCT Images Using a...

Mandibular odontogenic cysts can complicate surgical planning if located near the mandibular canal. Accurate delineation of both structures is therefore clinically important. This study presents a deep learning-based method for simultaneously segmenting mandibular odontogenic cysts and the mandibular canal in cone-beam computed tomography (CBCT) images. The developed method relies on a hybrid deep learning architecture integrating a ConvNeXt-Base encoder, a Dilated Context Block, a U-Net-based decoder, and a Deep Supervision mechanism. This framework simultaneously segments the mandibular canal and odontogenic cyst regions within the same architecture, promoting multi-scale contextual feature learning. We evaluated the performance of the proposed method using fivefold cross-validation experiments on a real-world clinical CBCT dataset comprising 103 patient images. We assessed segmentation performance using overlap-based metrics together with boundary-aware HD95 and ASSD metrics. The experimental results demonstrated that the model achieved a mean intersection over union (mIoU) of 76.28% ± 1.83%. Excluding the background class yielded macro IoU and macro F1-score values of 64.46% ± 2.74% and 77.00% ± 1.89%, respectively. Meanwhile, the weighted IoU and weighted F1-score values were 78.71% ± 4.25% and 87.63% ± 2.63%, respectively. In boundary-based evaluations, the mandibular canal had an HD95 of 2.44 ± 2.98 mm and an ASSD of 0.45 ± 0.30 mm. The corresponding values for odontogenic cysts were 5.59 ± 3.65 mm and 1.43 ± 0.88 mm, respectively. These results suggest that the mandibular canal is more consistently delineated, while cyst segmentation remains more challenging, particularly at irregular and low-contrast boundaries. The proposed framework allows for the simultaneous and objective segmentation of mandibular odontogenic cysts and the mandibular canal within a unified, multi-class CBCT analysis framework. By providing reproducible delineation of both structures, this method could serve as a technical foundation for future quantitative analyses of lesion-canal relationships. However, further validation using larger, multicenter, volumetric CBCT datasets is required before clinical translation.