An edge-driven 3D region growing approach for upper airways morphology and volume evaluation in patients with Pierre Robin sequence

Abstract

Pierre Robin sequence (PRS) is a pathological condition responsible for a sequence of clinical events, such as breathing and feeding difficulties, that must be addressed to give the patient at least a chance to survive. By using medical imaging techniques, in a non-intrusive way, the surgeon has the opportunity to obtain 3D views, reconstruction 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 implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D model of the upper airways. This method 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 (92.1733%) and Dice (94.6441%) similarity indices and specificity (96.8895%) and sensitivity (97.6682%) rates. The proposed method achieved an average computation time reduced by a 16x factor with respect to manual segmentation.