Real-time video enhancement for narrow-band imaging bronchoscopy

Narrow-band imaging (NBI), a relatively new bronchoscopy technology, offers superior visualization of vascular details in lesion areas along the airway walls compared to standard white light bronchoscopy. This empowers physicians to detect suspect lesions and characterize their underlying vascular structures for further indications of cancerous activity. Unfortunately, the bronchoscopic video stream suffers from blurring artifacts due to device and patient motions, resulting in low-resolution visualization of lesion areas. To address this problem, we present an image enhancement method for NBI bronchoscopy to improve: 1) visualization of vascular structures; 2) lesion detection; and 3) vessel segmentation. We adapted Real-ESRGAN, a single-image super-resolution network, to enhance bronchoscopic images in real-time. This involved a transfer learning approach to fine-tune a pre-trained model using our public NBI bronchial lesion database. The results, derived from bronchoscopic airway exam videos of 10 lung cancer patients, demonstrate significant improvement in the visual quality of super-resolved frames, particularly in vascular regions. Our quantitative analysis further shows enhanced vessel segmentation and lesion detection accuracy, with increased confidence scores. This method offers a practical, real-time solution for improving the diagnostic utility of NBI bronchoscopy by providing clearer, more detailed images. Thus, we integrated the method into an NBI video analysis system for aiding in the early detection and characterization of bronchial lesions.