Real-time surface reconstruction from stereo endoscopic images for intraoperative registration

Röhl, Sebastian; Bodenstedt, Sebastian; Suwelack, Stefan; Kenngott, Hannes; Mueller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie

doi:10.1117/12.877662

Cited by 21 publications

(25 citation statements)

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“…In contrast with current state-of-the-art methods, e.g. [10,11,12], instead of computing one set of robust and optimal matches, we retain all possible matches with their associated disparity (displacement d ∈ Z between matching points along the same horizontal line of the recitified images) and similarity measure (c ∈ [−1, 1]).…”

Section: Dense Matching Of Left and Right Stereo Imagesmentioning

confidence: 99%

Uncertainty-Encoded Augmented Reality for Robot-Assisted Partial Nephrectomy: A Phantom Study

Amir-Khalili¹,

Nosrati²,

Peyrat³

et al. 2013

Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions

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Abstract. In most robot-assisted surgical interventions, multimodal fusion of pre-and intra-operative data is highly valuable, affording the surgeon a more comprehensive understanding of the surgical scene observed through the stereo endoscopic camera. More specifically, in the case of partial nephrectomy, fusing pre-operative segmentations of kidney and tumor with the stereo endoscopic view can guide tumor localization and the identification of resection margins. However, the surgeons are often unable to reliably assess the levels of trust they can bestow on what is overlaid on the screen. In this paper, we present the proofof-concept of an uncertainty-encoded augmented reality framework and novel visualizations of the uncertainties derived from the pre-operative CT segmentation onto the surgeon's stereo endoscopic view. To verify its clinical potential, the proposed method is applied to an ex vivo lamb kidney. The results are contrasted to different visualization solutions based on crisp segmentation demonstrating that our method provides valuable additional information that can help the surgeon during the resection planning.

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Section: Dense Matching Of Left and Right Stereo Imagesmentioning

confidence: 99%

Uncertainty-Encoded Augmented Reality for Robot-Assisted Partial Nephrectomy: A Phantom Study

Amir-Khalili¹,

Nosrati²,

Peyrat³

et al. 2013

Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions

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show abstract

“…As previously proposed in relevant publications [7,8], the ideal matching of a patch from the left camera to the right camera must be consistent with matching the same patch from the right camera to the left. This doubles the computational complexity of the algorithm but introduces robustness to specular noise and occlusions.…”

Section: Robust Surface Reconstructionmentioning

confidence: 99%

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

Amir-Khalili

Peyrat

Hamarneh

et al. 2013

Abdominal Imaging. Computation and Clinical Applications

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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 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.

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“…In the last decades, some progress has been made using SfM (Structure from Motion) and Simultaneous Localization And Mapping (SLAM). In [4], the authors compute a corresponding point for each pixel using a recursive algorithm on undistorted and rectified images. To avoid the use of stereoscopic endoscope SFM methods have been employed for laparoscopic surface reconstruction.…”

Section: Related Workmentioning

confidence: 99%

Performance of Stereovision-based Soft Tissue 3D Reconstruction in Laparoscopy

Wang¹,

Liang²,

Zhang³

et al. 2017

dtcse

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Real-time surface reconstruction from stereo endoscopic images for intraoperative registration

Cited by 21 publications

References 0 publications

Uncertainty-Encoded Augmented Reality for Robot-Assisted Partial Nephrectomy: A Phantom Study

Uncertainty-Encoded Augmented Reality for Robot-Assisted Partial Nephrectomy: A Phantom Study

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

Performance of Stereovision-based Soft Tissue 3D Reconstruction in Laparoscopy

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