Self-Optimizing Reconstruction of Textured 3D Maps from Endoscopic Images
Real-time, computer assisted medical procedures present the potential for surgeons to not only rely on their experience, but also use additional information provided by a computer, resulting in more precise and time-efficient procedures. Endoscopic, minimally invasive applications increasingly rely on support regarding the location and orientationn of instruments within a surgical site from clinical navigation systems, visualising the instruments' positions as well as regions of interest and planned paths inside three-dimensional CT or MRI patient data. The necessary camera pose information is commonly supplied by external, electromagnetic or optical measuring systems and trackers attached to the instruments.
Visual Navigation (VINA) is a novel, purely image-based approach to navigation, aiming to substitute external measuring systems, while still providing the surgeon with all necessary information. One approach to solving this problem is to reconstruct the surrounding surgical site on-the-fly, and use the obtained 3D model for a comparison with CT or MRI patient data to extract movement information.
Developed at Scopis GmbH in Berlin, this master's thesis attempts to reconstruct bronchial tubes from low-resolution images captured by an electromagnetically tracked bronchoscope. Current 3D reconstruction techniques are reviewed and evaluated in terms of feasibility with respect to the setting. An approach to a solution is formulated and implemented, combining Structure from Motion and Shape from Shading techniques to generate a depth map for each input image. 3D coordinates computed from these depth maps are subsequently inserted into a customised octree structure based on the OctoMap framework, resulting in a full reconstruction of the viewed scenes.
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