Three-dimensional quantification of skeletal midfacial complex symmetry.

PURPOSE: Quantification of skeletal symmetry in a healthy population could have a strong impact on the reconstructive surgical procedures where mirroring of the contralateral healthy side acts as a clinical reference for the restoration of unilateral defects. Hence, the aim of this study was to three-dimensionally assess the symmetry of skeletal midfacial complex in skeletal class I patients. METHODS: A sample of 100 cone beam computed tomography (CBCT) scans (50 males, 50 females; age range: 19-40 years) were recruited. Automated segmentation of the skeletal midfacial complex was performed to create a three-dimensional (3D) virtual model using a convolutional neural network (CNN)-based segmentation tool. Thereafter, the segmented model was mirrored and registered to quantify skeletal symmetry using a color-coded conformance mapping based on a surface part comparison analysis. RESULTS: Overall, the mean and root-mean-square (RMS) differences between complete true and mirrored models were 0.14 ± 0.12 and 0.87 ± 0.21 mm, respectively. Female patients had a significantly more symmetrical midfacial complex (mean difference: 0.11 ± 0.1 mm, RMS: 0.81 ± 0.17 mm) compared to male patients (mean difference: 0.16 ± 0.13 mm, RMS: 0.94 ± 0.23 mm). No significant difference existed between left and right sides irrespective of the patient's gender. CONCLUSION: The comparison between true and mirrored complete and left/right split midfacial complex showed symmetry within a clinically acceptable range of 1 mm, which justifies the applicability of using the mirroring technique. The presented data could act as a reference guide for surgeons during planning of reconstructive surgical procedures and outcome assessment at follow-up.

Automatic segmentation of the pharyngeal airway space with convolutional neural network.

OBJECTIVES: This study proposed and investigated the performance of a deep learning based three-dimensional (3D) convolutional neural network (CNN) model for automatic segmentation of the pharyngeal airway space (PAS). METHODS: A dataset of 103 computed tomography (CT) and cone-beam CT (CBCT) scans was acquired from an orthognathic surgery patients database. The acquisition devices consisted of 1 CT (128-slice multi-slice spiral CT, Siemens Somatom Definition Flash, Siemens AG, Erlangen, Germany) and 2 CBCT devices (Promax 3D Max, Planmeca, Helsinki, Finland and Newtom VGi evo, Cefla, Imola, Italy) with different scanning parameters. A 3D CNN-based model (3D U-Net) was built for automatic segmentation of the PAS. The complete CT/CBCT dataset was split into three sets, training set (n = 48) for training the model based on the ground-truth observer-based manual segmentation, test set (n = 25) for getting the final performance of the model and validation set (n = 30) for evaluating the model's performance versus observer-based segmentation. RESULTS: The CNN model was able to identify the segmented region with optimal precision (0.97±0.01) and recall (0.96±0.03). The maximal difference between the automatic segmentation and ground truth based on 95% hausdorff distance score was 0.98±0.74mm. The dice score of 0.97±0.02 confirmed the high similarity of the segmented region to the ground truth. The Intersection over union (IoU) metric was also found to be high (0.93±0.03). Based on the acquisition devices, Newtom VGi evo CBCT showed improved performance compared to the Promax 3D Max and CT device. CONCLUSION: The proposed 3D U-Net model offered an accurate and time-efficient method for the segmentation of PAS from CT/CBCT images. CLINICAL SIGNIFICANCE: The proposed method can allow clinicians to accurately and efficiently diagnose, plan treatment and follow-up patients with dento-skeletal deformities and obstructive sleep apnea which might influence the upper airway space, thereby further improving patient care.