Accuracy Of Imaging Software for 3d Rendering of Tooth Structures, Usable in Clinical Settings

Aim: The aim of this study was to evaluate the segmentation accuracy of dentition testing four free-source semi-automatic software. Materials and methods: A total of 20 cone-beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of maxillary and mandibular dentition. The software tested were Invesalius, ITK-Snap, 3D Slicer and Seg3D. Each tooth model was also manually segmented (Mimics software) and set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the teeth models obtained with semi-automatic software and the GS model, and to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the tooth models compared to the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analysed to perform software comparisons. Result: Statistically significant differences were found in the volumetric and matching percentage data (p < 0,05). Invesalius was the most accurate software for 3D rendering of the dentition with a volumetric bias (Mimics) ranging from 4,59 mm3 to 85,79 mm3; instead, ITK-SNAP showed the higher volumetric bias, ranging from 30,22 mm3 to 319,83 mm3. The dis-matched area was mainly located at the radicular region of the teeth. Volumetric data showed excellent inter-software reliability with coefficient values ranging from 0,951 to 0,997. Conclusion: Different semi-automatic software algorithms could generate different patterns of inaccuracy error in the segmentation of teeth.