Rosalia Leonardi1, Antonino Lo Giudice2, Marco Farronato3, Vincenzo Ronsivalle2, Silvia Allegrini4, Giuseppe Musumeci5, Concetto Spampinato6. 1. Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy. Electronic address: rleonard@unict.it. 2. Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy. 3. Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Department of Biomedical, Surgical and Dental Sciences, School of Dentistry, University of Milan, Milan, Italy. 4. Private practice, Pisa, Italy. 5. Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Catania, Italy. 6. Department of Computer and Telecommunications Engineering, University of Catania, Catania, Italy.
Abstract
INTRODUCTION: This study aimed to test the accuracy of a new automatic deep learning-based approach on the basis of convolutional neural networks (CNN) for fully automatic segmentation of the sinonasal cavity and the pharyngeal airway from cone-beam computed tomography (CBCT) scans. METHODS: Forty CBCT scans from healthy patients (20 women and 20 men; mean age, 23.37 ± 3.34 years) were collected, and manual segmentation of the sinonasal cavity and pharyngeal subregions were carried out by using Mimics software (version 20.0; Materialise, Leuven, Belgium). Twenty CBCT scans from the total sample were randomly selected and used for training the artificial intelligence model file. The remaining 20 CBCT segmentation masks were used to test the accuracy of the CNN fully automatic method by comparing the segmentation volumes of the 3-dimensional models obtained with automatic and manual segmentations. The accuracy of the CNN-based method was also assessed by using the Dice score coefficient and by the surface-to-surface matching technique. The intraclass correlation coefficient and Dahlberg's formula were used to test the intraobserver reliability and method error, respectively. Independent Student t test was used for between-groups volumetric comparison. RESULTS: Measurements were highly correlated with an intraclass correlation coefficient value of 0.921, whereas the method error was 0.31 mm3. A mean difference of 1.93 ± 0.73 cm3 was found between the methodologies, but it was not statistically significant (P >0.05). The mean matching percentage detected was 85.35 ± 2.59 (tolerance 0.5 mm) and 93.44 ± 2.54 (tolerance 1.0 mm). The differences, measured as the Dice score coefficient in percentage, between the assessments done with both methods were 3.3% and 5.8%, respectively. CONCLUSIONS: The new deep learning-based method for automated segmentation of the sinonasal cavity and the pharyngeal airway in CBCT scans is accurate and performs equally well as an experienced image reader.
INTRODUCTION: This study aimed to test the accuracy of a new automatic deep learning-based approach on the basis of convolutional neural networks (CNN) for fully automatic segmentation of the sinonasal cavity and the pharyngeal airway from cone-beam computed tomography (CBCT) scans. METHODS: Forty CBCT scans from healthy patients (20 women and 20 men; mean age, 23.37 ± 3.34 years) were collected, and manual segmentation of the sinonasal cavity and pharyngeal subregions were carried out by using Mimics software (version 20.0; Materialise, Leuven, Belgium). Twenty CBCT scans from the total sample were randomly selected and used for training the artificial intelligence model file. The remaining 20 CBCT segmentation masks were used to test the accuracy of the CNN fully automatic method by comparing the segmentation volumes of the 3-dimensional models obtained with automatic and manual segmentations. The accuracy of the CNN-based method was also assessed by using the Dice score coefficient and by the surface-to-surface matching technique. The intraclass correlation coefficient and Dahlberg's formula were used to test the intraobserver reliability and method error, respectively. Independent Student t test was used for between-groups volumetric comparison. RESULTS: Measurements were highly correlated with an intraclass correlation coefficient value of 0.921, whereas the method error was 0.31 mm3. A mean difference of 1.93 ± 0.73 cm3 was found between the methodologies, but it was not statistically significant (P >0.05). The mean matching percentage detected was 85.35 ± 2.59 (tolerance 0.5 mm) and 93.44 ± 2.54 (tolerance 1.0 mm). The differences, measured as the Dice score coefficient in percentage, between the assessments done with both methods were 3.3% and 5.8%, respectively. CONCLUSIONS: The new deep learning-based method for automated segmentation of the sinonasal cavity and the pharyngeal airway in CBCT scans is accurate and performs equally well as an experienced image reader.