Deep learning-assisted CBCT segmentation provides reliable volumetric assessment of mandibular defects compared with micro-CT for 3D printing and surgical planning.

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Abstract

Accurate volumetric assessment of intraosseous lesions is crucial in various fields, including bone defect evaluation, surgical outcome prediction, treatment monitoring, and 3D model design. High volumetric accuracy is essential for CBCT in digital dentistry applications. However, there is a notable lack of studies investigating the accuracy of volume determination using CBCT. In this study, we examined the factors affecting CBCT volumetric accuracy, namely voxel size, lesion location, and segmentation techniques, to improve diagnostic protocols and optimize the clinical applications of this imaging modality. 28 artificial bone defects were created in the dry rabbit mandible in two regions (Anterior and Posterior). CBCT imaging was performed with standardized positioning at two voxel sizes (0.1 and 0.2 mm). regarding micro-CT imaging as the gold standard. Images were analyzed in DICOM format using ImageJ after preprocessing, and semi-automatic segmentation was conducted via Otsu thresholding with a manually defined external defect border. In Avizo, a ResNet18-encoded U-Net architecture (Avizo's Backboned U-Net implementation) was trained for the multiclass segmentation of the bone, background, and lesions. The volume calculations were based on the voxel counts. Volumetric measurements from CBCT showed no statistically significant difference from the micro-CT gold standard (p > 0.05). However, a significant underestimation of volume was observed when using a larger voxel size (0.2 mm) compared with a smaller voxel size (0.1 mm), irrespective of the segmentation software used (p < 0.05). The choice of software (ImageJ vs. Avizo's deep learning-assisted tool) did not significantly affect the measurements of the porosity. The location of the defect (anterior vs. posterior) also had no significant impact on the accuracy. CBCT is a reliable tool for the volumetric assessment of mandibular bone defects and demonstrates strong agreement with micro-CT. Clinically, our findings suggest that selecting a smaller voxel size (0.1 mm) is paramount for maximizing measurement accuracy in applications requiring high precision, such as surgical planning and 3D model fabrication. The implementation of a deep learning-assisted segmentation model proved to be a viable and efficient alternative to conventional semi-automatic methods, highlighting its potential to streamline the digital workflow in dentistry without compromising accuracy.

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