Excessive central airway collapse (ECAC) is a pathological condition marked by significant narrowing of the central airway during expiration. Current diagnostic methods rely on visual estimation of the luminal area during dynamic bronchoscopy, which often leads to interobserver variability, affecting both diagnosis and treatment decisions. Such variability can be reduced by computationally estimating areas from a segmentation of airways. In order to have reliable measures, segmentation methods should be able to adapt to the varying illumination conditions of intra-operative videos using a limited and sparse amount of annotated frames. While machine learning (ML) models can be trained on few data, they do not have the adaptability of deep learning (DL) approaches trained on larger datasets. In this study, we introduce a system for endoscopic dynamics assessment that leverages a ML approach to a DL model for the dynamic assessment of luminal area in bronchoscopy videos. Our DL system is based on a U-Net model trained using a semi-automated annotated database built upon segmentations obtained from a ML method trained using only 100 manually annotated images. The U-Net is compared to the ML method in terms of temporal continuity in 10 videos and quality of the segmentations of the 100 annotated images. The adaptability of Unet to illumination conditions is assessed on an independent set of 24 videos. Results demonstrate that U-Net has higher generalization, establishing it as a more effective tool for dynamic video segmentation and, thus, the clinical assessment of ECAC.