A Novel Patient-to-Image Surface Registration Technique for ENT- and Neuro-Navigation Systems: Proper Point Set in Patient Space

Choi, Ahnryul; Chae, Seungheon; Kim, Tae Hyong; Jung, Hyunwoo; Lee, Sangsik; Lee, Ki-Young; Mun, Joung-Hwan

doi:10.3390/app11125464

Cited by 6 publications

(10 citation statements)

References 27 publications

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“…This result showed that it was slightly better compared to the previous results that tried to reduce the SRE by extracting a new point-cloud. Yoo et al 7 and Choi et al 15 achieved 1.53-2.23 and 1.79-3.32 mm, respectively, using their novel surface registration methodologies. Additionally, the results proposed in this study are quite close to the point-registration standard of 1.0 mm or less, 46,47 which requires relatively high registration accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Yoo et al 7 . and Choi et al 15 . achieved 1.53–2.23 and 1.79–3.32 mm, respectively, using their novel surface registration methodologies.…”

Section: Discussionmentioning

confidence: 99%

“…They attempted to obtain a point‐cloud corresponding to that acquired by OTS. Additionally, Choi et al 15 . proposed a novel surface registration protocol base on the contact approach to extract the optimal point‐cloud in the patient space (information acquired by OTS).…”

Section: Introductionmentioning

confidence: 99%

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Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

Yoo

Sim

2022

Medical Physics

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Background Although the surface registration technique has the advantage of being relatively safe and the operation time is short, it generally has the disadvantage of low accuracy. Purpose This research proposes automated machine learning (AutoML)‐based surface registration to improve the accuracy of image‐guided surgical navigation systems. Methods The state‐of‐the‐art surface registration concept is that first, using a neural network model, a new point‐cloud that matches the facial information acquired by a passive probe of an optical tracking system (OTS) is extracted from the facial information obtained by computerized tomography. Target registration error (TRE) representing the accuracy of surface registration is then calculated by applying the iterative closest point (ICP) algorithm to the newly extracted point‐cloud and OTS information. In this process, the hyperparameters used in the neural network model and ICP algorithm are automatically optimized using Bayesian optimization with expected improvement to yield improved registration accuracy. Results Using the proposed surface registration methodology, the average TRE for the targets located in the sinus space and nasal cavity of the soft phantoms is 0.939 ± 0.375 mm, which shows 57.8% improvement compared to the average TRE of 2.227 ± 0.193 mm calculated by the conventional surface registration method (p < 0.01). The performance of the proposed methodology is evaluated, and the average TREs computed by the proposed methodology and the conventional method are 0.767 ± 0.132 and 2.615 ± 0.378 mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML‐based surface registration is also presented. Conclusion Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique.

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Section: Discussionmentioning

confidence: 99%

“…Yoo et al 7 . and Choi et al 15 . achieved 1.53–2.23 and 1.79–3.32 mm, respectively, using their novel surface registration methodologies.…”

Section: Discussionmentioning

confidence: 99%

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Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

Yoo

Sim

2022

Medical Physics

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“…The proposed method showed an obvious speed advantage (17.4s and 21.4s for the elastic phantom and five patients, respectively) as well as an improvement in registration accuracy (1.17 ± 0.04 mm and range 1.64 mm to 1.83 mm for the elastic phantom and five patients, respectively). Choi et al [20] extracted the optimal pointcloud that has the minimum distance from the point-cloud in image space. The optimal point-cloud was used to improve surface registration accuracy, and it was validated using a hemisphere mathematical model and a plastic facial phantom.…”

Section: Related Workmentioning

confidence: 99%

“…Accordingly, a surface registration method has been proposed [1,10]. Related studies have been conducted to improve registration accuracy that is relatively low compared to point registration [1,2,9,10,12,17,[19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Approach for Automated Coarse Registration (ACR) of Image-Guided Surgical Navigation

Yoo

Sim

2022

IEEE Access

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Coarse registration is the first step in determining the accuracy of surgical navigation. The purpose of this study was to present an automated coarse registration (ACR) methodology to improve the convenience and accuracy. For this purpose, a deep learning model based on a convolutional neural network was used. The input variable used for learning was virtual patient point-cloud (VPPC) generated based on medical image. Output variables were values of coordinate transformation obtained in the process of sending the VPPC to the surrounding space of a medical image. The ACR model consisted of a step of extracting global features of point-clouds from medical image and patient space and a step of predicting the information of 3-dimensional coordinate transformation through global features. The coefficients of determination that evaluated the similarity between predicted and actual rotation values on the x, y, and z axes were 0.993, 0.989, and 0.990, respectively. The coefficients of determination of the predicted and actual translation values on x, y, and z were 0.993, 0.989, and 0.994, respectively. As a result of coarse registration of three phantoms using the ACR, the registration errors between the patient and the computed tomography point-cloud were 3.813 ± 0.792, 3.786 ± 0.734, and 3.653 ± 0.668 mm, which were significantly improved over the conventional method's registration error (4.671 ± 0.738, 4.865 ± 0.776, and 4.670 ± 0.455 mm). The proposed method can provide convenience in the pre-operative preparation stage by automating coarse registration. It is expected that repeatability and reproducibility can be provided by eliminating random errors that might occur by the operator.

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“Image to patient” equal-resolution surface registration supported by a surface scanner: analysis of algorithm efficiency for computer-aided surgery

et al. 2022

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Purpose The “image to patient” registration procedure is crucial for the accuracy of surgical instrument tracking relative to the medical image while computer-aided surgery. The main aim of this work was to create an equal-resolution surface registration algorithm (ERSR) and analyze its efficiency. Methods The ERSR algorithm provides two datasets with equal, high resolution and approximately corresponding points. The registered sets are obtained by projection of a user-designed rectangle(s)-shaped uniform clouds of points on DICOM and surface scanner datasets. The tests of the algorithm were performed on a phantom with titanium microscrews. We analyzed the influence of DICOM resolution on the effect of the ERSR algorithm and compared the ERSR to standard paired-points landmark transform registration. The methods of analysis were Target Registration Error, distance maps, and their histogram evaluation. Results The mean TRE in case of ERSR equaled 0.8 ± 0.3 mm (resolution A), 0.8 ± 0.5 mm (resolution B), and 1.0 ± 0.7 mm (resolution C). The mean values were at least 0.4 mm lower than in the case of landmark transform registration. The distance maps between the model achieved from the scanner and the CT-based model were analyzed by histogram. The frequency of the first bin in a histogram of the distance map for ERSR was about 0.6 for all three resolutions of DICOM dataset and three times higher than in the case of landmark transform registration. The results were statistically analyzed using the Wilcoxon signed-rank test (alpha = 0.05). Conclusion The tests proved a statistically significant higher efficiency of equal resolution surface registration related to the landmark transform algorithm. It was proven that the lower resolution of the CT DICOM dataset did not degrade the efficiency of the ERSR algorithm. We observed a significantly lower response to decreased resolution than in the case of paired-points landmark transform registration.

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A Novel Patient-to-Image Surface Registration Technique for ENT- and Neuro-Navigation Systems: Proper Point Set in Patient Space

Cited by 6 publications

References 27 publications

Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

Automated machine learning (AutoML)‐based surface registration methodology for image‐guided surgical navigation system

A Deep Learning-Based Approach for Automated Coarse Registration (ACR) of Image-Guided Surgical Navigation

“Image to patient” equal-resolution surface registration supported by a surface scanner: analysis of algorithm efficiency for computer-aided surgery

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