Soft Tissue Deformation Tracking for Robotic Assisted Minimally Invasive Surgery

This paper presents a new framework for tracking soft tissue deformation in robotic assisted minimally invasive surgery. The method combines optical feature tracking based on stereo-laparoscope images and a constrained geometrical surface model that deforms with feature motion. This has the advantage of relying on reliable salient feature tracking while embedding underlying constraints on the tissue surface for deriving consistent temporal deformation. The proposed framework is resilient to occlusions and specular highlights. The accuracy and robustness of the proposed method are validated using a phantom heart model with known ground truth. To demonstrate the practical value of the method, example in vivo results are also provided.