Abstract
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.
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Amir-Khalili, A., Peyrat, JM., Hamarneh, G., Abugharbieh, R. (2013). 3D Surface Reconstruction of Organs Using Patient-Specific Shape Priors in Robot-Assisted Laparoscopic Surgery. In: Yoshida, H., Warfield, S., Vannier, M.W. (eds) Abdominal Imaging. Computation and Clinical Applications. ABD-MICCAI 2013. Lecture Notes in Computer Science, vol 8198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41083-3_21
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DOI: https://doi.org/10.1007/978-3-642-41083-3_21
Publisher Name: Springer, Berlin, Heidelberg
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