Soft tissue coverage on the segmentation accuracy of the 3D surface-rendered model from cone-beam CT

The aim of this study is to investigate the effect of soft tissue presence on the segmentation accuracy of the 3D hard tissue models from cone-beam computed tomography (CBCT).

Seven pairs of CBCT Digital Imaging and Communication in Medicine (DICOM) datasets, containing data of human cadaver heads and their respective dry skulls, were used. The effect of the soft tissue presence on the accuracy of the segmented models was evaluated by performing linear and angular measurements and by superimposition and color mapping of the surface discrepancies after splitting the mandible and maxillo-facial complex in the midsagittal plane.

The linear and angular measurements showed significant differences for the more posterior transversal measurements on the mandible (p < 0.01). By splitting and superimposing the maxillo-facial complex, the mean root-mean-square error (RMSE) as a measurement of inaccuracy decreased insignificantly from 0.936 to 0.922 mm (p > 0.05). The RMSE value for the mandible, however, significantly decreased from 1.240 to 0.981 mm after splitting (p < 0.01).

The soft tissue presence seems to affect the accuracy of the 3D hard tissue model obtained from a cone-beam CT, below a generally accepted level of clinical significance of 1 mm. However, this level of accuracy may not meet the requirement for applications where high precision is paramount.

Accuracy of CBCT-based 3D surface-rendered models, especially of the hard tissues, are crucial in several dental and medical applications, such as implant planning and virtual surgical planning on patients undergoing orthognathic and navigational surgeries. When used in applications where high precision is paramount, the effect of soft tissue presence should be taken into consideration during the segmentation process.