Dense 3D Depth Recovery for Soft Tissue Deformation During Robotically Assisted Laparoscopic Surgery

Recovering tissue deformation during robotic assisted minimally invasive surgery is an important step towards motion compensation and stabilization. This paper presents a practical strategy for dense 3D depth recovery and temporal motion tracking for deformable surfaces. The method combines image rectification with constrained disparity registration for reliable depth estimation. The accuracy and practical value of the technique is validated with a tissue phantom with known 3D geometry and motion characteristics. It has been shown that the performance of the proposed approach compares favorably against existing methods. Example results of the technique applied to in vivo robotic assisted minimally invasive surgery data are also provided.

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