3 March 2017 A system for endobronchial video analysis
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Abstract
Image-guided bronchoscopy is a critical component in the treatment of lung cancer and other pulmonary disorders. During bronchoscopy, a high-resolution endobronchial video stream facilitates guidance through the lungs and allows for visual inspection of a patient’s airway mucosal surfaces. Despite the detailed information it contains, little effort has been made to incorporate recorded video into the clinical workflow. Follow-up procedures often required in cancer assessment or asthma treatment could significantly benefit from effectively parsed and summarized video. Tracking diagnostic regions of interest (ROIs) could potentially better equip physicians to detect early airway-wall cancer or improve asthma treatments, such as bronchial thermoplasty. To address this need, we have developed a system for the postoperative analysis of recorded endobronchial video. The system first parses an input video stream into endoscopic shots, derives motion information, and selects salient representative key frames. Next, a semi-automatic method for CT-video registration creates data linkages between a CT-derived airway-tree model and the input video. These data linkages then enable the construction of a CT-video chest model comprised of a bronchoscopy path history (BPH) - defining all airway locations visited during a procedure - and texture-mapping information for rendering registered video frames onto the airwaytree model. A suite of analysis tools is included to visualize and manipulate the extracted data. Video browsing and retrieval is facilitated through a video table of contents (TOC) and a search query interface. The system provides a variety of operational modes and additional functionality, including the ability to define regions of interest. We demonstrate the potential of our system using two human case study examples.
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Patrick D. Byrnes, William E. Higgins, "A system for endobronchial video analysis", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351Q (3 March 2017); doi: 10.1117/12.2254975; https://doi.org/10.1117/12.2254975
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