ArticleProceedings of SPIE--the International Society for Optical Engineering
Toward real-time endoscopically-guided robotic navigation based on a 3D virtual surgical field model.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep Learning for Automatic Segmentation of Pituitary Adenomas: A Videomics Study.Operative neurosurgery (Hagerstown, Md.) · 2026Article
- Bound constrained bundle adjustment for reliable 3D reconstruction.Optics express · 2015Article
- Path Planning for Semi-automated Simulated Robotic Neurosurgery.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and SystemsArticle
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Authors and funding
4 authors.
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Abstract
The challenge is to accurately guide the surgical tool within the three-dimensional (3D) surgical field for robotically-assisted operations such as tumor margin removal from a debulked brain tumor cavity. The proposed technique is 3D image-guided surgical navigation based on matching intraoperative video frames to a 3D virtual model of the surgical field. A small laser-scanning endoscopic camera was attached to a mock minimally-invasive surgical tool that was manipulated toward a region of interest (residual tumor) within a phantom of a debulked brain tumor. Video frames from the endoscope provided features that were matched to the 3D virtual model, which were reconstructed earlier by raster scanning over the surgical field. Camera pose (position and orientation) is recovered by implementing a constrained bundle adjustment algorithm. Navigational error during the approach to fluorescence target (residual tumor) is determined by comparing the calculated camera pose to the measured camera pose using a micro-positioning stage. From these preliminary results, computation efficiency of the algorithm in MATLAB code is near real-time (2.5 sec for each estimation of pose), which can be improved by implementation in C++. Error analysis produced 3-mm distance error and 2.5 degree of orientation error on average. The sources of these errors come from 1) inaccuracy of the 3D virtual model, generated on a calibrated RAVEN robotic platform with stereo tracking; 2) inaccuracy of endoscope intrinsic parameters, such as focal length; and 3) any endoscopic image distortion from scanning irregularities. This work demonstrates feasibility of micro-camera 3D guidance of a robotic surgical tool.
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