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 image-guidance 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 intra-operative 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.
KeywordsSurface reconstruction computational stereo shape prior robot-assisted minimally invasive surgery
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