Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Soft Tissue Tracking for Minimally Invasive Surgery: Learning Local Deformation Online
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Heart surface motion estimation framework for robotic surgery employing meshless methods
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
International Journal of Robotics Research
Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Tissue deformation recovery with gaussian mixture model based structure from motion
AE-CAI'11 Proceedings of the 6th international conference on Augmented Environments for Computer-Assisted Interventions
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In the context of minimally invasive cardiac surgery, active vision-based motion compensation schemes have been proposed for mitigating problems related to physiological motion. However, robust and accurate visual tracking is a difficult task. The purpose of this paper is to present a hybrid tracker that estimates the heart surface deformation using the outputs of multiple visual tracking techniques. In the proposed method, the failure of an individual technique can be circumvented by the success of others, enabling the robust estimation of the heart surface deformation with increased spatial resolution. In addition, for coping with the absence of visual information due to motion blur or occlusions, a temporal heart motion model is incorporated as an additional support for the visual tracking task. The superior performance of the proposed technique compared to existing techniques individually is demonstrated through experiments conducted on recorded images of an in vivo minimally invasive CABG using the DaVinci robotic platform.