Soft tissue motion tracking with application to tablet-based incision planning in laser surgery

Schoob, Andreas; Laves, Max-Heinrich; Kahrs, Lüder A.; Ortmaier, Tobias

doi:10.1007/s11548-016-1420-5

Cited by 12 publications

(18 citation statements)

References 38 publications

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“…Furthermore, it helps to detect the occluded regions, which gives the tracking accuracy of 2.45 mm. But this is a time‐consuming process because mesh deformation is twice for every organ …”

Section: Literature Reviewmentioning

confidence: 99%

“…It achieved processing time of 3 to 5 frames per second and 1 mm of target overlay error. However, it does not offer depth perception of targeted area because of occlusion . Bernhardt et al proposed a system to automatic registration and reconstruction of CT data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…But this is a time-consuming process because mesh deformation is twice for every organ. 11,12 Wang et al 5 implemented Tracking Learning detection (TLD) algorithm and Iterative Closet Point (ICP) algorithm to track the boundary of organ model and to get 3D model of organ, respectively. These algorithms showed the improvement in surgeon perception towards the target area.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, it does not offer depth perception of targeted area because of occlusion. [5][6][7][8][9][10][11][12][13][14][15][16] Bernhardt et al 17 proposed a system to automatic registration and reconstruction of CT data. This system gave the 0.1 mm in printing 3D model and registration errors below 15 pixels.…”

Section: Literature Reviewmentioning

confidence: 99%

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A novel augmented reality to visualize the hidden organs and internal structure in surgeries

Singh

Alsadoon

Prasad

et al. 2020

Robotics Computer Surgery

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Background Augmented reality (AR) is still a primarily theoretical concept in areas such as bowel, liver, gallbladder, and jaw surgeries because of the limitation of visualization accuracy of hidden organs and internal structures. This paper aims to improve the cutting accuracy, visualizing accuracy, and processing time of the augmented video. Methodology The proposed system consists of an enhanced block‐matching algorithm (BMA) with ghosting map technique. Results Results proved that proposed system reduced the visualization error, which ranges from 1.48 to 1.83 mm against the existing system visualization error 1.67 to 2.0. Similarly, the processing time also improved 59 to 72 ms/frame over the 50 to 58 ms/frame. Conclusion This study showed the improvement and solved the problem soft tissue reconstruction and visualization on the AR video that used in bowel and gallbladder surgeries.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A novel augmented reality to visualize the hidden organs and internal structure in surgeries

Singh

Alsadoon

Prasad

et al. 2020

Robotics Computer Surgery

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“…5 An advanced future application is envisioned by soft tissue laser ablation with compensated tissue motion. 6 By extracting three orthogonal slices from the regarded volume, the triplanar view (TV) as common representation of volumetric images is generated. As this works well for static single-shot 3D volumes, in time-varying volumes the surgeon can easily lose track of the volume of interest (VOI) due to observer or tissue movement.…”

Section: Introductionmentioning

confidence: 99%

Feature tracking for automated volume of interest stabilization on 4D-OCT images

et al. 2017

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A common representation of volumetric medical image data is the triplanar view (TV), in which the surgeon manually selects slices showing the anatomical structure of interest. In addition to common medical imaging such as MRI or computed tomography, recent advances in the field of optical coherence tomography (OCT) have enabled live processing and volumetric rendering of four-dimensional images of the human body. Due to the region of interest undergoing motion, it is challenging for the surgeon to simultaneously keep track of an object by continuously adjusting the TV to desired slices. To select these slices in subsequent frames automatically, it is necessary to track movements of the volume of interest (VOI). This has not been addressed with respect to 4D-OCT images yet. Therefore, this paper evaluates motion tracking by applying state-of-the-art tracking schemes on maximum intensity projections (MIP) of 4D-OCT images. Estimated VOI location is used to conveniently show corresponding slices and to improve the MIPs by calculating thin-slab MIPs. Tracking performances are evaluated on an in-vivo sequence of human skin, captured at 26 volumes per second. Among investigated tracking schemes, our recently presented tracking scheme for soft tissue motion provides highest accuracy with an error of under 2.2 voxels for the first 80 volumes. Object tracking on 4D-OCT images enables its use for sub-epithelial tracking of microvessels for image-guidance.

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Preclinical Performance Evaluation of a Robotic Endoscope for Non-Contact Laser Surgery

Kundrat

Graesslin²,

Schoob

et al. 2020

Ann Biomed Eng

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Soft tissue motion tracking with application to tablet-based incision planning in laser surgery

Cited by 12 publications

References 38 publications

A novel augmented reality to visualize the hidden organs and internal structure in surgeries

A novel augmented reality to visualize the hidden organs and internal structure in surgeries

Feature tracking for automated volume of interest stabilization on 4D-OCT images

Preclinical Performance Evaluation of a Robotic Endoscope for Non-Contact Laser Surgery

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