AI-based Automatic Segmentation of Sinonasal Cavity in Cone-Beam Computed Tomography

The evaluation of the sinonasal cavity and paranasal sinuses is crucial for diagnosing nasal disturbances and tumors through Cone-Beam Computed Tomography (CBCT). This technology provides 3D volumes for better visualization, helping rapidly diagnose different diseases. The recent advancements in artificial intelligence methodologies applied to these medical images permit rapid and accurate segmentation of the sinonasal cavity in 3D CBCT studies, even when noise characteristics are unknown and difficult to eliminate. This study proposes an efficient AI-based method using a 2D U-Net to rapidly segment the sinonasal cavity in 3D CBCT studies. The method was validated on a dataset of 102 subjects, demonstrating high accuracy (about 99.3 %) and high efficiency, performing the segmentation in under 20 seconds.