Automated system of the determination of maxillary sinus morphometric parameters

Abstract

Was to compare manual, semi-automatic and automatic methods for determining the maxillary sinus volume using cone beam computed tomography (CBCT). CBCT images from 48 patients (96 maxillary sinuses) with no history of sinus and alveolar bone surgery, who were presented to Minsk medical centers, were used in this study. Neural network training was performed on CBCT scans of 42 patients (84 maxillary sinuses).The height, depth and width of the sinus were measured manually on CBCT scans of 6 patients (12 maxillary sinuses). Maxillary sinus volume (V) was calculated by the formula: V=height´depth´1/3 width. Semi-automatic segmentation was carried out by an expert radiologist. The convolutional neural network technology was applied for maxillary sinus automatic segmentation. The largest values were revealed by using the automatic method for sinus volume measurement. These values were within the 95% confidence interval (±4.29 cm3) of the average sinus volume obtained from semi-automatic method. The data obtained using the convolutional neural network technique (artificial intelligence) has a high correlation with the results of sinus morphometric analysis acquired through manual and semi-automatic methods. Automatic maxillary sinus segmentation technique does not require special user knowledge. This method is reproducible and it is implemented in a short time interval.