One Step before 3D Printing-Evaluation of Imaging Software Accuracy for 3-Dimensional Analysis of the Mandible: A Comparative Study Using a Surface-to-Surface Matching Technique

Antonino Lo Giudice, Vincenzo Ronsivalle, Cristina Grippaudo, Alessandra Lucchese, Simone Muraglie, Manuel O Lagravère, Gaetano Isola, Antonino Lo Giudice, Vincenzo Ronsivalle, Cristina Grippaudo, Alessandra Lucchese, Simone Muraglie, Manuel O Lagravère, Gaetano Isola

Abstract

The accuracy of 3D reconstructions of the craniomaxillofacial region using cone beam computed tomography (CBCT) is important for the morphological evaluation of specific anatomical structures. Moreover, an accurate segmentation process is fundamental for the physical reconstruction of the anatomy (3D printing) when a preliminary simulation of the therapy is required. In this regard, the objective of this study is to evaluate the accuracy of four different types of software for the semiautomatic segmentation of the mandibular jaw compared to manual segmentation, used as a gold standard. Twenty cone beam computed tomography (CBCT) with a manual approach (Mimics) and a semi-automatic approach (Invesalius, ITK-Snap, Dolphin 3D, Slicer 3D) were selected for the segmentation of the mandible in the present study. The accuracy of semi-automatic segmentation was evaluated: (1) by comparing the mandibular volumes obtained with semi-automatic 3D rendering and manual segmentation and (2) by deviation analysis between the two mandibular models. An analysis of variance (ANOVA) was used to evaluate differences in mandibular volumetric recordings and for a deviation analysis among the different software types used. Linear regression was also performed between manual and semi-automatic methods. No significant differences were found in the total volumes among the obtained 3D mandibular models (Mimics = 40.85 cm3, ITK-Snap = 40.81 cm3, Invesalius = 40.04 cm3, Dolphin 3D = 42.03 cm3, Slicer 3D = 40.58 cm3). High correlations were found between the semi-automatic segmentation and manual segmentation approach, with R coefficients ranging from 0,960 to 0,992. According to the deviation analysis, the mandibular models obtained with ITK-Snap showed the highest matching percentage (Tolerance A = 88.44%, Tolerance B = 97.30%), while those obtained with Dolphin 3D showed the lowest matching percentage (Tolerance A = 60.01%, Tolerance B = 87.76%) (p < 0.05). Colour-coded maps showed that the area of greatest mismatch between semi-automatic and manual segmentation was the condylar region and the region proximate to the dental roots. Despite the fact that the semi-automatic segmentation of the mandible showed, in general, high reliability and high correlation with the manual segmentation, caution should be taken when evaluating the morphological and dimensional characteristics of the condyles either on CBCT-derived digital models or physical models (3D printing).

Keywords: 3D biomaterials; accuracy; dental 3D rendering; dental 3D scanner; dentistry; manufacturing; models; printing; scanner; segmentation.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Manual segmentation of mandibular jaw (ground truth); (a), coronal view; (b), axial view; (c), sagittal view; (d), 3D rendered mandible model.
Figure 2
Figure 2
Landmarking four points on 3D mandibular model superimposition: 1–2, the geometric center of the left and right metal foramina; 3–4, left and right mandibular lingual at the inner surface of ramus.
Figure 3
Figure 3
Each 3D mandibular model obtained from semi-automatic segmentation was superimposed to its ground truth model (manual segmentation) in order to reliably remove alveolar processes and teeth. (A), occlusal plane (OP) constructed by selecting three points, respectively, the mesio-buccal cusp tip of the mandibular first molars and the interproximal point between the two mandibular central incisors; (B,C), the OP was manually translated on the Y-axis (vertical axis) until it reached the apical position of the lowest dento-gingival junction; (D), sagittal view of the defined horizontal cutting plane; (E), construction of a distal cutting plane passing through the tangent line to distal wall of the second mandibular molars; (F), removal of the alveolar process according to the two defined cutting planes.
Figure 4
Figure 4
Surface-based deviation analysis between 3D mandibular models obtained with semi-automatic segmentation and its ground truth model (manual segmentation). (A) ITK-Snap; (B) Invesalius; (C) Dolphin 3D; (D) Slicer 3D. The green of the color scale bar, on the right, represents the range of tolerance. Left side—color map set to a range of tolerance of ±0.2 mm. Right side—color map set to a range of tolerance of ±0.5 mm.
Figure 5
Figure 5
Flow chart of the entire process involved in this study.

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