Abstract
With increasing demand on intra-operative navigation and motion compensation during robotic assisted minimally invasive surgery, real-time 3D deformation recovery remains a central problem. Currently the majority of existing methods rely on salient features, where the inherent paucity of distinctive landmarks implies either a semi-dense reconstruction or the use of strong geometrical constraints. In this study, we propose a gaze-contingent depth reconstruction scheme by integrating human perception with semi-dense stereo and p-q based shading information. Depth inference is carried out in real-time through a novel application of Bayesian chains without smoothness priors. The practical value of the scheme is highlighted by detailed validation using a beating heart phantom model with known geometry to verify the performance of gaze-contingent 3D surface reconstruction and deformation recovery.
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Visentini-Scarzanella, M., Mylonas, G.P., Stoyanov, D., Yang, GZ. (2009). i-BRUSH: A Gaze-Contingent Virtual Paintbrush for Dense 3D Reconstruction in Robotic Assisted Surgery. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_44
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DOI: https://doi.org/10.1007/978-3-642-04268-3_44
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