3D Surface Reconstruction of Organs Using Patient-Specific Shape Priors in Robot-Assisted Laparoscopic Surgery

With the advent of robot-assisted laparoscopic surgery (RALS), intra-operative stereo endoscopy is becoming a ubiquitous imaging modality in abdominal interventions. This high resolution intra-operative imaging modality enables the reconstruction of 3D soft-tissue surface geometry with the help of computer vision techniques. This reconstructed surface is a prerequisite for many clinical applications such as imageguidance with cross-modality registration, telestration, expansion of the surgical scene by stitching/mosaicing, and collision detection. Reconstructing the surface geometry from camera information alone remains a very challenging problem in RALS mainly due to a small baseline between the optical centres of the cameras, presence of blood and smoke, specular highlights, occlusion, and smooth/textureless regions. In this paper, we propose a method for increasing the overall surface reconstruction accuracy by incorporating patient specific shape priors extracted from pre-operative images. Our method is validated on an in silico phantom and we show that the combination of both pre-operative and intraoperative data significantly improves surface reconstruction as compared to the ground truth. Finally, we verify the clinical potential of the proposed method in the context of abdominal surgery in a phantom study of an ex vivo lamb kidney.