Evaluation of a marker-less, intra-operative, augmented reality guidance system for robot-assisted laparoscopic radical prostatectomy

Kalia, Megha; Mathur, Prateek; Tsang, Keith; Black, Peter C.; Navab, Nassir; Salcudean, Septimiu E.

doi:10.1007/s11548-020-02181-4

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…On the contrary, surface‐based methods focus on the intraoperative perspective rather than on preoperative data, because the surface is intraoperatively reconstructed directly on laparoscopic images and registered only at a later stage 37,38 . Finally, the volume‐based methodologies are the most complex ones, as they require an intraoperative imaging system in addition to the endoscope, to better locate the hidden structures 39 . On the other hand, a double registration is needed, between preoperative and intraoperative volumes and between the preoperative volume and the endoscopic image.…”

Section: Related Workmentioning

confidence: 99%

“… 37 , 38 Finally, the volume‐based methodologies are the most complex ones, as they require an intraoperative imaging system in addition to the endoscope, to better locate the hidden structures. 39 On the other hand, a double registration is needed, between preoperative and intraoperative volumes and between the preoperative volume and the endoscopic image.…”

Section: Related Workmentioning

confidence: 99%

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A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

Padovan

Marullo

Tanzi

et al. 2022

Robotics Computer Surgery

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Introduction The current study presents a deep learning framework to determine, in real‐time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real counterpart. The resulting augmented video flow is streamed back to the surgeon as a support during laparoscopic robot‐assisted procedures. Methods This framework exploits semantic segmentation and, thereafter, two techniques, based on Convolutional Neural Networks and motion analysis, were used to infer the rotation. Results The segmentation shows optimal accuracies, with a mean IoU score greater than 80% in all tests. Different performance levels are obtained for rotation, depending on the surgical procedure. Discussion Even if the presented methodology has various degrees of precision depending on the testing scenario, this work sets the first step for the adoption of deep learning and augmented reality to generalise the automatic registration process.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

Padovan

Marullo

Tanzi

et al. 2022

Robotics Computer Surgery

View full text Add to dashboard Cite

show abstract

“…The collective effect of testing methodology and limited experimental coordinate transformations between data input/output reduces error propagation and maximizes optoelectronic accuracy (Figure 2, Table ). [91][92][93][94][95][96][97] Application Accuracy: Clinical Operative Platform Transitioning from controlled laboratory conditions to the dynamic variability of a clinical operative environment presents a different set of application challenges for maintaining peak optoelectronic accuracy. Unique to robotic-assisted surgery and in contrast to the laboratory setting, the intraoperative process requires considerably more steps in the transference of optoelectronic kinematic data.…”

Section: Application Accuracy: Basic Scientific Laboratory Platformmentioning

confidence: 99%

Accuracy of Robotic-Assisted Spinal Surgery—Comparison to TJR Robotics, da Vinci Robotics, and Optoelectronic Laboratory Robotics

2021

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Background: The optoelectronic camera source and data interpolation serve as the foundation for navigational integrity in the robotic-assisted surgical platform. The objective of the current systematic review serves to provide a basis for the numerical disparity that exists when comparing the intrinsic accuracy of optoelectronic cameras: accuracy observed in the laboratory setting versus accuracy in the clinical operative environment. It is postulated that there exists a greater number of connections in the optoelectronic kinematic chain when analyzing the clinical operative environment to the laboratory setting. This increase in data interpolation, coupled with intraoperative workflow challenges, reduces the degree of accuracy based on surgical application and to that observed in controlled musculoskeletal kinematic laboratory investigations.Methods: Review of the PubMed and Cochrane Library research databases was performed. The exhaustive literature compilation obtained was then vetted to reduce redundancies and categorized into topics of intrinsic optoelectronic accuracy, registration accuracy, musculoskeletal kinematic platforms, and clinical operative platforms.Results: A total of 147 references make up the basis for the current analysis. Regardless of application, the common denominators affecting overall optoelectronic accuracy are intrinsic accuracy, registration accuracy, and application accuracy. Intrinsic accuracy of optoelectronic tracking equaled or was less than 0.1 mm of translation and 0.18 of rotation per fiducial. Controlled laboratory platforms reported 0.1 to 0.5 mm of translation and 0.18-1.08 of rotation per array. There is a huge falloff in clinical applications: accuracy in robotic-assisted spinal surgery reported 1.5 to 6.0 mm of translation and 1.58 to 5.08 of rotation when comparing planned to final implant position. Total Joint Robotics and da Vinci urologic robotics computed accuracy, as predicted, lies between these two extremes-1.02 mm for da Vinci and 2 mm for MAKO.Conclusions: Navigational integrity and maintenance of fidelity of optoelectronic data is the cornerstone of roboticassisted spinal surgery. Transitioning from controlled laboratory to clinical operative environments requires an increased number of steps in the optoelectronic kinematic chain and error potential. Diligence in planning, fiducial positioning, system registration, and intraoperative workflow have the potential to improve accuracy and decrease disparity between planned and final implant position. The key determining factors limiting navigation resolution accuracy are highlighted by this Cochrane research analysis.

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“…Xiaohui Zhang et al [12] optimized the volume deformation algorithm and proposed an augmented reality projection method adapted to laparoscopic partial nephrectomy surgery navigation to provide visual support for laparoscopic surgery. Megha Kalia et al [13] proposed a calibration method to solve the problem of the visual field blind area of the Leonardo da Vinci surgical robot for prostate surgery, which helps the doctor avoid the step of endoscope removal during operation.…”

Section: Introductionmentioning

confidence: 99%

“…Megha Kalia et al. [13] proposed a calibration method to solve the problem of the visual field blind area of the Leonardo da Vinci surgical robot for prostate surgery, which helps the doctor avoid the step of endoscope removal during operation.…”

Section: Introductionmentioning

confidence: 99%

Augmented reality display of neurosurgery craniotomy lesions based on feature contour matching

Zhang

Sun

Liu

2021

Cognitive Comp and Systems

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Traditional neurosurgical craniotomy primarily uses two-dimensional cranial medical images to estimate the location of a patient's intracranial lesions. Such work relies on the experience and skills of the doctor and may result in accidental injury to important intracranial physiological tissues. To help doctors more intuitively determine patient lesion information and improve the accuracy of surgical route formulation and craniotomy safety, an augmented reality method for displaying neurosurgery craniotomy lesions based on feature contour matching is proposed. This method uses threshold segmentation and region growing algorithms to reconstruct a 3-D Computed tomography image of the patient's head. The augmented reality engine is used to adjust the reconstruction model's relevant parameters to meet the doctor's requirements and determine the augmented reality matching method for feature contour matching. By using the mobile terminal to align the real skull model, the virtual lesion model is displayed. Using the designed user interface, doctors can view the patient's personal information and can zoom in, zoom out, and rotate the virtual model. Therefore, the patient's lesions information can be visualized accurately, which provides a visual basis for preoperative preparation.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Evaluation of a marker-less, intra-operative, augmented reality guidance system for robot-assisted laparoscopic radical prostatectomy

Cited by 11 publications

References 19 publications

A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

Accuracy of Robotic-Assisted Spinal Surgery—Comparison to TJR Robotics, da Vinci Robotics, and Optoelectronic Laboratory Robotics

Augmented reality display of neurosurgery craniotomy lesions based on feature contour matching

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