Asymmetrical surface scanning registration for image-guided otologic surgery: A phantom study

Matsumoto, Nozomu; Yamashita, Makoto; Cho, Byunghyun; Komune, Noritaka; Hashizume, Makoto

doi:10.1016/j.anl.2020.01.007

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Image registration based on points of interest (POIs) is a common method in medical image registration technology that requires the selection of some feature points in the image before registration. In image-guided otological surgery, the target registration error of measurement points at various depths from the surface of the head is measured to achieve a higher degree of registration accuracy [18]. To reduce the time required, both preoperatively and intraoperatively, skin fiducials may offer an efficient alternative method of navigation registration to bone fiducials [19].…”

Section: Introductionmentioning

confidence: 99%

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Chen

et al. 2020

IEEE Access

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Automatic detection of specific anatomical points of interest (POIs) in head magnetic resonance imaging (MRI) is a technical bottleneck in medical image registration and big data analysis for head diseases. A technique for automatically retrieving POIs in head MRI scans is explored in this study. A Haar-like feature of the image is introduced to generate the feature description vector of the POI, and the most appropriate standard vector is selected from multiple marked images. A fixed five-point joint feature description is proposed for solid POI detection, and an adaptive three-point joint feature description is proposed for cavity POI detection. A total of 516 head MRI volumes were used for anatomical point detection. A solid point detection experiment was conducted using the POIs of the right/left internal acoustic pore (RIA/LIA). The POIs of the right/left ascending segment of the internal carotid artery in the posterior cavernous sinus (RAS/LAS) were used in a cavity point detection experiment. The experimental results show that the accuracies of the solid point detection for LIA and RIA are 81.8% and 84.7%, respectively. Those of cavity point detection for LAS and RAS are 66.7% and 76.2%. The performance of the proposed method is better than those of BRIEF and SIFT algorithm. The proposed method can facilitate the marking of anatomical points for doctors, thus providing technical support for head image automatic registration and big data analysis for head diseases.

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Section: Introductionmentioning

confidence: 99%

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Chen

et al. 2020

IEEE Access

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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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Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks

Taleb,

Leclerc,

Hussein

et al. 2024

Eur Arch Otorhinolaryngol

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Asymmetrical surface scanning registration for image-guided otologic surgery: A phantom study

Cited by 3 publications

References 17 publications

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

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

Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks

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