Dense GPU-enhanced surface reconstruction from stereo endoscopic images for intraoperative registration
Purpose: In laparoscopic surgery, soft tissue deformations substantially change the surgical site, thus impeding the use of preoperative planning during intraoperative navigation. Extracting depth information from endoscopic images and building a surface model of the surgical field-of-view is one wa...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1 March 2012
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| In: |
Medical physics
Year: 2012, Jahrgang: 39, Heft: 3, Pages: 1632-1645 |
| ISSN: | 2473-4209 |
| DOI: | 10.1118/1.3681017 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1118/1.3681017 Verlag, Volltext: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.3681017 
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| Verfasserangaben: | Sebastian Röhl, Sebastian Bodenstedt, Stefan Suwelack, Hannes Kenngott, Beat P. Müller‐Stich, Rüdiger Dillmann, Stefanie Speidel |
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| 100 | 1 | |a Röhl, Sebastian |e VerfasserIn |0 (DE-588)1037861965 |0 (DE-627)756366712 |0 (DE-576)391902814 |4 aut | |
| 245 | 1 | 0 | |a Dense GPU-enhanced surface reconstruction from stereo endoscopic images for intraoperative registration |c Sebastian Röhl, Sebastian Bodenstedt, Stefan Suwelack, Hannes Kenngott, Beat P. Müller‐Stich, Rüdiger Dillmann, Stefanie Speidel |
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| 520 | |a Purpose: In laparoscopic surgery, soft tissue deformations substantially change the surgical site, thus impeding the use of preoperative planning during intraoperative navigation. Extracting depth information from endoscopic images and building a surface model of the surgical field-of-view is one way to represent this constantly deforming environment. The information can then be used for intraoperative registration. Stereo reconstruction is a typical problem within computer vision. However, most of the available methods do not fulfill the specific requirements in a minimally invasive setting such as the need of real-time performance, the problem of view-dependent specular reflections and large curved areas with partly homogeneous or periodic textures and occlusions. Methods: In this paper, the authors present an approach toward intraoperative surface reconstruction based on stereo endoscopic images. The authors describe our answer to this problem through correspondence analysis, disparity correction and refinement, 3D reconstruction, point cloud smoothing and meshing. Real-time performance is achieved by implementing the algorithms on thegpu. The authors also present a new hybrid cpu-gpu algorithm that unifies the advantages of the cpu and the gpu version. Results: In a comprehensive evaluation usingin vivo data, in silico data from the literature and virtual data from a newly developed simulation environment, the cpu, the gpu, and the hybrid cpu-gpu versions of the surface reconstruction are compared to a cpu and a gpu algorithm from the literature. The recommended approach toward intraoperative surface reconstruction can be conducted in real-time depending on the image resolution (20 fps for the gpu and 14fps for the hybrid cpu-gpu version on resolution of 640 × 480). It is robust to homogeneous regions without texture, large image changes, noise or errors from camera calibration, and it reconstructs the surface down to sub millimeter accuracy. In all the experiments within the simulation environment, the mean distance to ground truth data is between 0.05 and 0.6 mm for the hybrid cpu-gpu version. The hybrid cpu-gpu algorithm shows a much more superior performance than its cpu and gpu counterpart (mean distance reduction 26% and 45%, respectively, for the experiments in the simulation environment). Conclusions: The recommended approach for surface reconstruction is fast, robust, and accurate. It can represent changes in the intraoperative environment and can be used to adapt a preoperative model within the surgical site by registration of these two models. | ||
| 650 | 4 | |a Architectures of general purpose stored programme computers | |
| 650 | 4 | |a biological tissues | |
| 650 | 4 | |a Biomedical engineering | |
| 650 | 4 | |a Calibration | |
| 650 | 4 | |a Cameras | |
| 650 | 4 | |a devices or methods | |
| 650 | 4 | |a Digital computing or data processing equipment or methods | |
| 650 | 4 | |a e.g. endoscopes | |
| 650 | 4 | |a e.g. tourniquets | |
| 650 | 4 | |a endoscopes | |
| 650 | 4 | |a Endoscopic imaging | |
| 650 | 4 | |a endoscopic procedures | |
| 650 | 4 | |a Illuminating arrangements therefor | |
| 650 | 4 | |a Image analysis | |
| 650 | 4 | |a Image data processing or generation | |
| 650 | 4 | |a image reconstruction | |
| 650 | 4 | |a Image reconstruction | |
| 650 | 4 | |a image registration | |
| 650 | 4 | |a image-guided therapy | |
| 650 | 4 | |a in general | |
| 650 | 4 | |a Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection | |
| 650 | 4 | |a intraoperative registration | |
| 650 | 4 | |a Liver | |
| 650 | 4 | |a medical computing | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a Medical image reconstruction | |
| 650 | 4 | |a Medical image smoothing | |
| 650 | 4 | |a Medical imaging | |
| 650 | 4 | |a mesh generation | |
| 650 | 4 | |a Reconstruction | |
| 650 | 4 | |a Registration | |
| 650 | 4 | |a Smoothing | |
| 650 | 4 | |a smoothing methods | |
| 650 | 4 | |a specially adapted for specific applications | |
| 650 | 4 | |a surface reconstruction | |
| 650 | 4 | |a Surface reconstruction | |
| 650 | 4 | |a surgery | |
| 650 | 4 | |a Surgical instruments | |
| 650 | 4 | |a Visual imaging | |
| 700 | 1 | |a Kenngott, Hannes Götz |d 1979- |e VerfasserIn |0 (DE-588)141469994 |0 (DE-627)62780117X |0 (DE-576)324023065 |4 aut | |
| 700 | 1 | |a Müller, Beat P. |d 1971- |e VerfasserIn |0 (DE-588)14066209X |0 (DE-627)70374819X |0 (DE-576)317992287 |4 aut | |
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