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.

Automated summary

The study proposed and investigated a 3D convolutional neural network model for automatic segmentation of the pharyngeal airway space. The CNN model showed high precision and recall in identifying the segmented region, with Newtom VGi evo CBCT device outperforming others. This method provides an accurate and time-efficient way to diagnose and treat upper airway problems.