The effect of intraoperative imaging on surgical navigation for laparoscopic liver resection surgery

Teatini, Andrea; Pelanis, Egidijus; Aghayan, Davit L.; Kumar, Rahul Prasanna; Palomar, Rafael; Fretland, Åsmund Avdem; Edwin, Bjørn; Elle, Ole Jakob

doi:10.1038/s41598-019-54915-3

Cited by 44 publications

(32 citation statements)

References 32 publications

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“…Overall, considering all the error sources [12]: optical tracking systems introduce approximately around 1.5-2 mm of error [7], hand-eye calibration could also introduce approximately 1-2 mm of error, pivot calibration introduced 0.8 mm, registration, camera and OTS localisation calibrations, IPD calibration, the accuracy of the navigation tool presents promising results for diagnostic assessment, but not as of yet for surgical procedures (where an accuracy inferior to five millimetres is generally acceptable [2]). The errors due to user inaccuracy or physical correspondent positions [11] may be reduced through better visualisation capabilities in HoloLens 2.…”

Section: Discussionmentioning

confidence: 99%

“…In surgical scenarios, the accuracy will improve considerably with the trackers placed directly on the bone, rather than the skin for the diagnostic case. However, with suitable trackers, we believe that suitable leg markerplates would reduce relative motion between bones and skin, and since bones do not deform greatly, as compared to softtissue [2], rigid registration is suitable for this clinical field. Improvements and testing of multiple leg tracking solutions will be explored in future studies.…”

Section: Discussionmentioning

confidence: 99%

“…Diagnosis of orthopaedic conditions are largely dependent on the experience of the clinician and the outcome of the surgery is instrument tracking technologies, such as NDI 's Polaris Spectra optical tracking (Northern Digital Inc. (NDI), Waterloo, Ontario, Canada), to reduce the need of intraoperative imaging. These systems have been used for several years in multiple fields of application [1][2][3]. They allow lowering unnecessary radiation while also improving surgical safety and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Mixed reality as a novel tool for diagnostic and surgical navigation in orthopaedics

et al. 2021

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Purpose This study presents a novel surgical navigation tool developed in mixed reality environment for orthopaedic surgery. Joint and skeletal deformities affect all age groups and greatly reduce the range of motion of the joints. These deformities are notoriously difficult to diagnose and to correct through surgery. Method We have developed a surgical tool which integrates surgical instrument tracking and augmented reality through a head mounted display. This allows the surgeon to visualise bones with the illusion of possessing “X-ray” vision. The studies presented below aim to assess the accuracy of the surgical navigation tool in tracking a location at the tip of the surgical instrument in holographic space. Results Results show that the average accuracy provided by the navigation tool is around 8 mm, and qualitative assessment by the orthopaedic surgeons provided positive feedback in terms of the capabilities for diagnostic use. Conclusions More improvements are necessary for the navigation tool to be accurate enough for surgical applications, however, this new tool has the potential to improve diagnostic accuracy and allow for safer and more precise surgeries, as well as provide for better learning conditions for orthopaedic surgeons in training.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mixed reality as a novel tool for diagnostic and surgical navigation in orthopaedics

et al. 2021

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“…Computer assistance in laparoscopic surgery requires scene understanding from images to display critical areas to surgeons during manual navigation and planning, in augmented reality scenarios [1], and to generate safe trajectories for robot assisted surgeries [2]. Recognition and segmentation of different organs and tissue types in laparoscopic images are important sub-problems of image based scene understanding [3].…”

Section: Problemmentioning

confidence: 99%

Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery

Scheikl

Laschewski

Kisilenko

et al. 2020

Current Directions in Biomedical Engineering

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Semantic segmentation of organs and tissue types is an important sub-problem in image based scene understanding for laparoscopic surgery and is a prerequisite for context-aware assistance and cognitive robotics. Deep Learning (DL) approaches are prominently applied to segmentation and tracking of laparoscopic instruments. This work compares different combinations of neural networks, loss functions, and training strategies in their application to semantic segmentation of different organs and tissue types in human laparoscopic images in order to investigate their applicability as components in cognitive systems. TernausNet-11 trained on Soft-Jaccard loss with a pretrained, trainable encoder performs best in regard to segmentation quality (78.31% mean Intersection over Union [IoU]) and inference time (28.07 ms) on a single GTX 1070 GPU.

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“…Two commercial IGS designed for open liver surgery [ 23 , 24 ], have been adapted for LLR with studies demonstrating comparable accuracy to open surgery [ 7 , 22 ]. These systems however are limited by the need for separate screens to demonstrate image guidance [ 7 ] and the use of manual registration [ 7 , 22 ] which is a source of errors and delay to the intraoperative workflow [ 25 ]. To address these issues an IGS is being developed with capabilities for AR and semi-automatic registration [ 26 , 27 ].…”

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confidence: 99%

Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study

et al. 2020

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Background The laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration. Methods Intraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes. Results The primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference − 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound. Conclusion The technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.

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The effect of intraoperative imaging on surgical navigation for laparoscopic liver resection surgery

Cited by 44 publications

References 32 publications

Mixed reality as a novel tool for diagnostic and surgical navigation in orthopaedics

Mixed reality as a novel tool for diagnostic and surgical navigation in orthopaedics

Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery

Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study

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