Computer aided detection of endobronchial valves

The ability to automatically detect and monitor implanted devices may serve an important role in patient care and the evaluation of device and treatment efficacy. The purpose of this research was to develop a system for the automated detection of one-way endobronchial valves implanted as part of a clinical trial for less invasive lung volume reduction. Volumetric thin section CT data was obtained for 275 subjects; 95 subjects implanted with 246 devices were used for system development and 180 subjects implanted with 354 devices were reserved for testing. The detection process consisted of pre-processing, pattern-recognition based detection, and a final device selection. Following the pre-processing, a set of classifiers were trained using AdaBoost to discriminate true devices from false positives (such as calcium deposits). The classifiers in the cascade used simple features (the mean or max attenuation) computed near control points relative to a template model of the valve. Visual confirmation of the system output served as the gold standard. FROC analysis was performed for the evaluation; the system could be set so the mean sensitivity was 96.5% with a mean of 0.18 false positives per subject. These generic device modeling and detection techniques may be applicable to other devices and useful for monitoring the placement and function of implanted devices.