Purpose: Recovering tissue deformation during robotic-assisted minimally invasive surgery procedures is important for providing intra-operative guidance, enabling in vivo imaging modalities and enhanced robotic control. The tissue motion can also be used to apply motion stabilization and to prescribe dynamic constraints for avoiding critical anatomical structures.
Methods: Image-based methods based independently on salient features or on image intensity have limitations when dealing with homogeneous soft tissues or complex reflectance. In this paper, we use a triangular geometric mesh model in order to combine the advantages of both feature and intensity information and track the tissue surface reliably and robustly.
Results: Synthetic and in vivo experiments are performed to provide quantitative analysis of the tracking accuracy of our method, and we also show exemplar results for registering multispectral images where there is only a weak image signal.
Conclusion: Compared to traditional methods, our hybrid tracking method is more robust and has improved convergence in the presence of larger displacements, tissue dynamics and illumination changes.
Keywords: Illumination compensation; Minimally invasive surgery; Multispectral imaging; Non-rigid surface tracking.