A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.

OBJECTIVES

To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.

MATERIALS AND METHODS

175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening. A DCNN algorithm adopting 2.5D structure was trained for automatic segmentation. Automatic measuring algorithms were further developed to evaluate the clinical reliability of the DCNN.

RESULTS

The median Dice Similarity Coefficient (DSC) for the air cavity, mucosa, teeth and maxillary bone segmentation were 0.990, 0.850, 0.961 and 0.953, respectively. The Intra-class Correlation Coefficien (ICC) of all automatic measuring algorithms exceeded 0.975. The 95% confidence interval (95%CI) of all volumetric metric bias were within ± 0.5 cm, of all 2D metric bias were within ± 1 mm. The DCNN also produced satisfying outcome for notably incomplete MS and edentulous alveolar crest.

CONCLUSIONS

The DCNN provided clinically reliable results. The automatic measuring algorithms could reveal 3D information embedded in CBCT 2D planes on the basis of automatic segmentation.

CLINICAL RELEVANCE

This platform helps dentists to conduct instant 3D reconstruction and automatic measuring of 3D clinical parameters of MS and adjacent structures.