Airway label prediction in video bronchoscopy using point cloud data

Abstract Point clouds are a common 3D representation being increasingly used in various fields. However, they are rarely applied in medical applications. In video bronchoscopy, point cloud data can help to guide lung procedures more accurately by using 3D shape information instead of solely relying on RGB image data. This is especially useful because the appearance of tissues can vary greatly and thus becomes difficult to interpret. Current methods often require patient-specific CT scans and electromagnetic tracking, which limits their use in places such as intensive care units. In this work, we present a deep learning method that predicts airway labels directly from point cloud data without using CT scans or tracking systems. This makes our method more flexible and reliable, even in difficult conditions. Using 3D data, we reduce the impact of lighting, camera quality, and patient differences, promising better results during medical procedures.