Method for endobronchial video parsing

Endoscopic examination of the lungs during bronchoscopy produces a considerable amount of endobronchial video. A physician uses the video stream as a guide to navigate the airway tree for various purposes such as general airway examinations, collecting tissue samples, or administering disease treatment. Aside from its intraoperative utility, the recorded video provides high-resolution detail of the airway mucosal surfaces and a record of the endoscopic procedure. Unfortunately, due to a lack of robust automatic video-analysis methods to summarize this immense data source, it is essentially discarded after the procedure. To address this problem, we present a fully-automatic method for parsing endobronchial video for the purpose of summarization. Endoscopic-shot segmentation is first performed to parse the video sequence into structurally similar groups according to a geometric model. Bronchoscope-motion analysis then identifies motion sequences performed during bronchoscopy and extracts relevant information. Finally, representative key frames are selected based on the derived motion information to present a drastically reduced summary of the processed video. The potential of our method is demonstrated on four endobronchial video sequences from both phantom and human data. Preliminary tests show that, on average, our method reduces the number of frames required to represent an input video sequence by approximately 96% and consistently selects salient key frames appropriately distributed throughout the video sequence, enabling quick and accurate post-operative review of the endoscopic examination.