An Edge-driven 3D Region-Growing Approach for Upper Airway Morphology and Volume Evaluation in Patients with Pierre Robin Sequence

Pierre Robin Sequence (PRS) is a pathological condition characterized by craniofacial dysmorphism, micro/retrognathia, glossoptosis and cleft palate. The underdeveloped mandible, identifiable in PRS, is responsible for a sequence of clinical events, such as breathing and feeding difficulties. These difficulties, due to upper airway obstruction and malformation, are the first ones to be addressed to give the patient at least a chance to survive. Nowadays, medical imaging plays an increasingly important role in because of its possibility to compute and display anatomical information by reconstructing the structures of interest. Using volumetric reconstructions, in a completely non-intrusive way, the surgeon has the opportunity to obtain 3D views of the Regions Of Interest (ROIs), useful to increase understanding of the PRS patient's condition. In this paper a semi-automatic approach for segmentation of the upper airways is proposed. The developed and implemented approach uses an edge-driven 3D Region-Growing algorithm to segment ROIs and 3D Volume-Rendering technique to reconstruct and display the corresponding 3D model of the upper airways. This method, providing information about segmented surface/volume and three-dimensional anatomical views, can be used to integrate information inside a Medical Decision Support System, making it possible to enhance medical evaluation. The effectiveness of the proposed segmentation approach was evaluated using Jaccard and Dice similarity indices (mean values were 92.1733% and 94.6441% respectively) and specificity and sensitivity rates (mean values were 96.8895% and 97.6682% respectively). The proposed method achieved an average computation time reduced by a 16x factor with respect to manual segmentation.