Deep Learning for in vivo Bronchial Carinae Detection in Flexible Bronchoscopy

Early lung cancer detection strongly increases survival rate. During a navigational bronchoscopy, pulmonologists perform tissue sampling for biopsies based on preoperative medical images. The bronchial carina is an airway structure that appears at each ronchus bifurcation. It is an important landmark to detect during navigations as it indicates the need to choose between multiple paths and keep track of the position in the lungs. In this paper, we assessed various deep learning pipelines including the use of semi-supervised, segmentation and recurrent methods under different setups to perform ronchial carinae detection. In contrast to most previous works that focus on phantoms, cadavers or virtual images, we exploit a large corpus of proprietary in vivo data captured during real endoscopic procedures using a mini probe. To the best of our knowledge, it is the first work that deals with this quantity of real and challenging data. After performing a comparison study, we conclude that the best performance to detect bronchial carinae is achieved by a semisupervised pipeline that leverages the ability of nnU-Net to solve segmentation tasks, coupled with Gated Recurrent Units that extracts temporal contexts from image sequences.