Deep monocular 3D reconstruction for assisted navigation in bronchoscopy

Marco Visentini Scarzanella, Takamasa Sugiura, Toshimitsu Kaneko, Shin-ichiro Koto

Abstract: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architecture that projects input frames onto the domain of CT renderings, thus allowing offline training from patient-specific CT data.