A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system

Liu, Yinlong; Song, Zhijian; Wang, Manning

doi:10.1080/24699322.2017.1389411

Cited by 23 publications

(26 citation statements)

References 17 publications

(50 reference statements)

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“…The current results are similar to those from our earlier publication (2.49 ± 1.07 mm for fiducial markers and 5.03 ± 2.30 mm for surface matching) [ 14 ]. Although, since then, a number of publications have reported relatively high accuracy of surface matching systems [ 1 , 5 , 8 , 9 , 11 ]; most of these do not compare to the use of fiducial markers, and none of these report on actual measurements in a true neurosurgical setting. Instead, they use cadaver measurements, phantom measurements, calculated estimations of accuracy or measurements in non-surgical cases such as Gamma Knife treatment.…”

Section: Discussionmentioning

confidence: 99%

Current accuracy of surface matching compared to adhesive markers in patient-to-image registration

Mongen

Willems

2019

Acta Neurochir

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Object In the past, the accuracy of surface matching has been shown to be disappointing. We aimed to determine whether this had improved over the years by assessing application accuracy of current navigation systems, using either surface matching or point-pair matching. Methods Eleven patients, scheduled for intracranial surgery, were included in this study after a power analysis had shown this small number to be sufficient. Prior to surgery, one additional fiducial marker was placed on the scalp, the “target marker,” where the entry point of surgery was to be expected. Using one of three different navigation systems, two patient-to-image registration procedures were performed: one based on surface matching and one based on point-pair matching. Each registration procedure was followed by the digitization of the target marker’s location, allowing calculation of the target registration error. If the system offered surface matching improvement, this was always used; and for the two systems that routinely offer an estimate of neuronavigation accuracy, this was also recorded. Results The error in localizing the target marker using point-pair matching or surface matching was respectively 2.49 mm and 5.35 mm, on average ( p < 0.001). In those four cases where an attempt was made to improve the surface matching, the error increased to 6.35 mm, on average. For the seven cases where the system estimated accuracy, this estimate did not correlate with target registration error ( R 2 = 0.04, p = 0.67). Conclusion The accuracy of navigation systems has not improved over the last decade, with surface matching consistently yielding errors that are twice as large as when point-pair matching with adhesive markers is used. These errors are not reliably reflected by the systems own prediction, when offered. These results are important to make an informed choice between image-to-patient registration strategies, depending on the type of surgery at hand.

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Section: Discussionmentioning

confidence: 99%

Current accuracy of surface matching compared to adhesive markers in patient-to-image registration

Mongen

Willems

2019

Acta Neurochir

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“…This manual selection increases the registration time, elicits some inconvenience, results in mismatch sometimes, and makes automatic registration impossible. 17 In order to overcome the aforementioned defect, a fast, accurate, and automatic markerless surface-matching registration method including keypoints extraction, coarse registration, and fine registration was proposed. The proposed method greatly decreases the registration time, improves the performance of the surface-matching method, saves the time of preoperative preparation and eases the surgeon's workload.…”

Section: Discussionmentioning

confidence: 99%

“…The TRE, reflects the registration accuracy of a specific position in the image and patient spaces, is the most import and clinically relevant measure. 28 There are some other automatic spatial registration methods without using skin markers 12,13,16,17 introduced in the introduction. The TRE of the proposed method is lower than IV.…”

Section: Discussionmentioning

confidence: 99%

“…So it usually needs manually selecting anatomic landmarks on the patient's head to obtain a reasonable initial pose for the fine registration. This manual selection increases the registration time, elicits some inconvenience, results in mismatch sometimes, and makes automatic registration impossible 17 . In order to overcome the aforementioned defect, a fast, accurate, and automatic markerless surface‐matching registration method including keypoints extraction, coarse registration, and fine registration was proposed.…”

Section: Discussionmentioning

confidence: 99%

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A robust automated surface‐matching registration method for neuronavigation

Fan

Yao

2020

Medical Physics

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The surface-matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient-to-image registration. Methods: A coarse-to-fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three-dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4-points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine-registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method. Results: Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 AE 0.04 mm, and the average registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient's head surface were 1.70 AE 0.32 mm, 1.83 AE 0.38 mm, 1.64 AE 0.3 mm, 1.67 AE 0.35 mm, and 1.72 AE 0.31 mm, respectively, and the average registration time was 21.4 s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy. Conclusions: The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.

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“…During the registration process, the operators could perform precise adjustments based on the visualized contour of the radiopaque image of intaglio surface of template that was presented in the form of a continuous layer on the dental ridge. As the entire edentulous soft tissue surface can be used as available large landmarks, the digital registration was facilitated 3,10,18 . Furthermore, compared to the limited numbers of the point‐based markers, the surface‐based matching condition allowed the utilization of numerous fiducial points, potentially enhancing the accuracy of image registration 3,6,19 .…”

Section: Discussionmentioning

confidence: 99%

Comparison of the Accuracy of Image Registration Methods for Merging Optical Scan and Radiographic Data in Edentulous Jaws

Woo

Mai

Lee

2020

Journal of Prosthodontics

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Purpose: The image registration of optical scans to radiographic images is essential for performing computer-guided implant surgery. This study aimed to evaluate the effect of different image matching conditions on the accuracy of image registration for computer-guided implant surgery in completely edentulous jaws. Materials and Methods: The optical scan image of a completely edentulous study model was registered to the respective cone-beam computed tomography data using three different image matching conditions: small point (SP), large point (LP), and entire surface (ES). For the SP and LP groups, gutta-percha markers (1.0 and 3.0 mm in diameter) were attached to a base template, and a radiopaque impression material was relined on the intaglio surface of template in the ES group. Image registration was performed by 20 operators in the images obtained from each group at an interval of 2 weeks (n = 20 in each group), and the registration accuracy was assessed by calculating the aligned position of the edentulous arch image. One-way analysis of variance with Tukey post hoc tests was used to compare the results among the groups (α = 0.05). Results: The mean registration error was significantly larger in the SP group (0.52 ± 0.19 mm) than in the LP group (0.29 ± 0.08 mm) and ES group (0.27 ±0.06 mm) (F = 24.689, p < 0.001). No difference was found between the LP and ES groups. The image matching discrepancy was more homogeneously distributed on the arch in the ES group than in the other groups. Conclusion: The accuracy of image registration is affected by the size of the congruent area shown in the optical scan and radiographic images. The entire surface-based matching method is more accurate as compared to the small point-based matching method in the image registration for implant planning in full edentulous jaws.

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A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system

Cited by 23 publications

References 17 publications

Current accuracy of surface matching compared to adhesive markers in patient-to-image registration

Current accuracy of surface matching compared to adhesive markers in patient-to-image registration

A robust automated surface‐matching registration method for neuronavigation

Comparison of the Accuracy of Image Registration Methods for Merging Optical Scan and Radiographic Data in Edentulous Jaws

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