Robust Surface-Matching Registration Based on the Structure Information for Image-Guided Neurosurgery System

Chen, Xinrong; Yang, Fengxia; Zhang, Ziqun; Bai, Baodan; Guo, Lei

doi:10.1142/s0219519421400091

Cited by 6 publications

(6 citation statements)

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“…During surgery, surgeons can fine-tune the needle’s posture directly through hand gesture interaction. The generation of the surgical navigation interface is based on our previous research ( Liu et al, 2017 ; Chen et al, 2021 ). This paper centers on the attainment of contactless HRI objectives, specifically, the detection of gestures and the subsequent control of surgical robots.…”

Section: Methodsmentioning

confidence: 99%

A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition

Zhang

Chen

et al. 2023

Front. Neurosci.

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Robot-assisted minimally invasive surgery (RAMIS) has gained significant traction in clinical practice in recent years. However, most surgical robots rely on touch-based human-robot interaction (HRI), which increases the risk of bacterial diffusion. This risk is particularly concerning when surgeons must operate various equipment with their bare hands, necessitating repeated sterilization. Thus, achieving touch-free and precise manipulation with a surgical robot is challenging. To address this challenge, we propose a novel HRI interface based on gesture recognition, leveraging hand-keypoint regression and hand-shape reconstruction methods. By encoding the 21 keypoints from the recognized hand gesture, the robot can successfully perform the corresponding action according to predefined rules, which enables the robot to perform fine-tuning of surgical instruments without the need for physical contact with the surgeon. We evaluated the surgical applicability of the proposed system through both phantom and cadaver studies. In the phantom experiment, the average needle tip location error was 0.51 mm, and the mean angle error was 0.34 degrees. In the simulated nasopharyngeal carcinoma biopsy experiment, the needle insertion error was 0.16 mm, and the angle error was 0.10 degrees. These results indicate that the proposed system achieves clinically acceptable accuracy and can assist surgeons in performing contactless surgery with hand gesture interaction.

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Section: Methodsmentioning

confidence: 99%

A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition

Zhang

Chen

et al. 2023

Front. Neurosci.

Self Cite

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“…During surgery, surgeons can fine-tune the needle's posture directly through hand gesture interaction. The generation of the surgical navigation interface is based on our previous research (Liu et al, 2017;Chen et al, 2021). This paper centers on the attainment of contactless HRI objectives, specifically, the detection of gestures and the subsequent control of surgical robots.…”

Section: System Workflowmentioning

confidence: 99%

Advanced Methods and Applications for Neurointelligence

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…4 It has also been used in many image-guided surgery (IGS) systems to achieve image-to-patient registration. [5][6][7][8] Image-to-patient registration aligns the preoperative images to intra-operative patient anatomy. This is typically done by aligning some corresponding structures existing in both the image space and the patient space.…”

Section: Introductionmentioning

confidence: 99%

Rigid point cloud registration based on correspondence cloud for image‐to‐patient registration in image‐guided surgery

Li,

Wang

2024

Medical Physics

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BackgroundImage‐to‐patient registration aligns preoperative images to intra‐operative anatomical structures and it is a critical step in image‐guided surgery (IGS). The accuracy and speed of this step significantly influence the performance of IGS systems. Rigid registration based on paired points has been widely used in IGS, but studies have shown its limitations in terms of cost, accuracy, and registration time. Therefore, rigid registration of point clouds representing the human anatomical surfaces has become an alternative way for image‐to‐patient registration in the IGS systems.PurposeWe propose a novel correspondence‐based rigid point cloud registration method that can achieve global registration without the need for pose initialization. The proposed method is less sensitive to outliers compared to the widely used RANSAC‐based registration methods and it achieves high accuracy at a high speed, which is particularly suitable for the image‐to‐patient registration in IGS.MethodsWe use the rotation axis and angle to represent the rigid spatial transformation between two coordinate systems. Given a set of correspondences between two point clouds in two coordinate systems, we first construct a 3D correspondence cloud (CC) from the inlier correspondences and prove that the CC distributes on a plane, whose normal is the rotation axis between the two point clouds. Thus, the rotation axis can be estimated by fitting the CP. Then, we further show that when projecting the normals of a pair of corresponding points onto the CP, the angle between the projected normal pairs is equal to the rotation angle. Therefore, the rotation angle can be estimated from the angle histogram. Besides, this two‐stage estimation also produces a high‐quality correspondence subset with high inlier rate. With the estimated rotation axis, rotation angle, and the correspondence subset, the spatial transformation can be computed directly, or be estimated using RANSAC in a fast and robust way within only 100 iterations.ResultsTo validate the performance of the proposed registration method, we conducted experiments on the CT‐Skull dataset. We first conducted a simulation experiment by controlling the initial inlier rate of the correspondence set, and the results showed that the proposed method can effectively obtain a correspondence subset with much higher inlier rate. We then compared our method with traditional approaches such as ICP, Go‐ICP, and RANSAC, as well as recently proposed methods like TEASER, SC2‐PCR, and MAC. Our method outperformed all traditional methods in terms of registration accuracy and speed. While achieving a registration accuracy comparable to the recently proposed methods, our method demonstrated superior speed, being almost three times faster than TEASER.ConclusionsExperiments on the CT‐Skull dataset demonstrate that the proposed method can effectively obtain a high‐quality correspondence subset with high inlier rate, and a tiny RANSAC with 100 iterations is sufficient to estimate the optimal transformation for point cloud registration. Our method achieves higher registration accuracy and faster speed than existing widely used methods, demonstrating great potential for the image‐to‐patient registration, where a rigid spatial transformation is needed to align preoperative images to intra‐operative patient anatomy.

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Robust Surface-Matching Registration Based on the Structure Information for Image-Guided Neurosurgery System

Cited by 6 publications

References 20 publications

A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition

A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition

Advanced Methods and Applications for Neurointelligence

Rigid point cloud registration based on correspondence cloud for image‐to‐patient registration in image‐guided surgery

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