研究者詳細 - 松野　隆幸

論文 - 松野　隆幸

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Proposal of new topology information Face-list for manipulation planning of deformable string tying 査読

Junxiang Wang, Tomoya Shirakawa, Tomotoshi Watanabe, Yuichiro Toda, Takayuki Matsuno

16 ( 1 ) 257 - 272 2023年7月

トポロジカルモデルと結び目不変量を用いたマニピュレーションのためのロープの形状認識

松野隆幸, 玉置大地, 新井史人, 福田敏男

計測自動制御学会論文集 41 ( 4 ) 366 - 372 2005年4月

Comparison of robotic versus manual needle insertion for CT-guided intervention: prospective randomized trial

Takao Hiraki, Yusuke Matsui, Jun Sakurai, Koji Tomita, Mayu Uka, Soichiro Kajita, Noriyuki Umakoshi, Toshihiro Iguchi, Michihiro Yoshida, Kota Sakamoto, Takayuki Matsuno, Tetsushi Kamegawa

Radiology Advances 2 ( 2 ) 2025年3月

掲載種別：研究論文（学術雑誌）出版者・発行元：Oxford University Press (OUP)

Abstract

Background

Robotic needle insertion under CT guidance has been developed, but data on comparison with manual insertion are still lacking.

Purpose

To compare robotic versus manual needle insertion for CT fluoroscopy-guided intervention, primarily in terms of insertion accuracy.

Materials and Methods

This was a prospective study between May 2020 and March 2023 at a single site. The cohort comprised 22 patients undergoing CT (Aquilion One or Aquilion CX; Canon Medical Systems) fluoroscopy-guided biopsy, who were randomly allocated to either the robotic or manual group. The robot used (Zerobot; Medicalnet Okayama) is not yet commercially available. A biopsy introducer needle was inserted by 1 of 3 physicians using a remote-control robot in the robotic group, versus by 1 of 3 different physicians by hand in the manual group. The primary endpoint was needle insertion accuracy, which was defined as the 3-dimensional Euclidean distance between a predetermined target point and the needle tip after insertion. The non-inferiority of robotic insertion to manual one was then tested. Adverse events were evaluated. Statistical comparisons were made between the 2 groups.

Results

Technical success and pathological findings were confirmed in all patients of the 2 groups. The mean and SD of needle insertion were 4.8 mm ± 2.6 in the robotic group and 7.0 mm ± 3.1 in the manual group (P &lt; .001). The mean difference in accuracy between the 2 groups (robotic minus manual group) was −2.1 mm (95% CI, −4.7 to 0.4). Effective dose to physicians was zero in all cases in the robotic group, while median dose was 1.0 µSv in the manual group (P &lt; .001). Dose length product to patients was not significantly different between the 2 groups (P = .100). No major adverse events were observed.

Conclusion

Robotic needle insertion was non-inferior to manual insertion in terms of accuracy, while it effectively eliminated radiation exposure to physicians.

Trial registration number

jRCT2062200013

DOI： 10.1093/radadv/umaf010

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その他リンク： https://academic.oup.com/radadv/article-pdf/2/2/umaf010/62871840/umaf010.pdf

Development of an Underwater Environment Analysis System Based on Neural Network Using Omnidirectional Camera

Kento Utsunomiya, Yuichiro Toda, Takayuki Matsuno

2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&amp;ISIS) 1 - 3 2024年11月

Global Path Planning Based on Rough Terrain Perception Using Growing Neural Gas

Haruka Ozaki, Yuichiro Toda, Dalin Zhou, Takayuki Matsuno

2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&amp;ISIS) 1 - 3 2024年11月

Case Study on a Navigation System using Growing Neural Gas

Koki Sakamoto, Yuichiro Toda, Makoto Tsujimoto, Kazuhiko Taniguchi, Takayuki Matsuno

2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&amp;ISIS) 1 - 3 2024年11月

Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot

Koki Ozasa, Yuichiro Toda, Yoshimasa Nakamura, Toshiki Masuda, Hirohide Konishi, Takayuki Matsuno

IEEE Access 12 171824 - 171835 2024年

Robotic systems in interventional oncology: a narrative review of the current status

Yusuke Matsui, Tetsushi Kamegawa, Koji Tomita, Mayu Uka, Noriyuki Umakoshi, Takahiro Kawabata, Kazuaki Munetomo, Toshihiro Iguchi, Takayuki Matsuno, Takao Hiraki

International Journal of Clinical Oncology 2023年4月

Contact detection algorithm for needle puncturing robot 査読

T. Matsuno, N. Kido, T. Kamegawa, T. Hiraki, T. Fukuda

Communications in Information and Systems 22 ( 4 ) 477 - 497 2023年1月

Growing Neural Gas based Traversability Clustering for an Autonomous Robot.

Koki Ozasa, Yuichiro Toda, Takayuki Matsuno

IJCNN 1 - 6 2023年

Growing Neural Gasに基づく3次元空間知覚と2次元環境地図との統合

久保平大智, 小笹航輝, 中村佳雅, 益田俊樹, 小西博英, 戸田雄一郎, 松野隆幸

ロボティクス・メカトロニクス講演会講演概要集 2023 2A1-G02 2023年

Growing neural gas based navigation system in unknown terrain environment for an autonomous mobile robot 査読

Yuichiro Toda, Koki Ozasa, Takayuki Matsuno

Artificial Life and Robotics 2022年11月

Automatic Puncture Needle Detection by Image Processing Using Deep Learning and CT Values 査読

Kotaro Mayumi, Takayuki Matsuno, Tetsushi Kamegawa, Ken’ichi, Morooka, Takao Hiraki, Yuichiro Toda

Proc of 33rd 2022 International Symposium on Micro-NanoMechatronics and Human Science 2022年11月

Environmental Map Learning Method based on Growing Neural Gas for a Mobile Robot 査読

Qi Li, Yuichiro Toda, Keisuke Nagao, Takayuki Matsuno

2022 International Joint Conference on Neural Networks (IJCNN) 2022年7月

Estimation of Needle Puncturing Form during Slight Needle Movement Based on Force Data for Robotic Automated Puncturing Function 査読

Takayuki Matsuno, Hikaru Murakami, Tetsushi Kamegawa, Nanako Sakai, Takao Hiraki, Yuichiro Toda

Journal of Medical Robotics Research 07 ( 02n03 ) 2022年6月

Growing Neural Gas with Different Topologies for 3D Space Perception

Yuichiro Toda, Akimasa Wada, Hikari Miyase, Koki Ozasa, Takayuki Matsuno, Mamoru Minami

Applied Sciences 12 ( 3 ) 1705 2022年2月

出版者・発行元：MDPI AG

<jats:p>Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research.</jats:p>

DOI： 10.3390/app12031705

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その他リンク： https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20K19894/

Multilayer Batch Learning Growing Neural Gas for Learning Multiscale Topologies 査読

Yuichiro Toda, Takayuki Matsuno, Mamoru Minami

Journal of Advanced Computational Intelligence and Intelligent Informatics 25 ( 6 ) 1011 - 1023 2021年11月

Estimation of Needle Puncturing Form Based on Force Data during Slight Needle Movement 査読

Takayuki Matsuno, Hikaru Murakami, Tetsushi Kamegawa, Takaaki Miyamoto, Nanako Sakai, Mamoru Minami, Takao Hiraki

proc of 2021 International Symposium on Medical Robotics 1 - 7 2021年11月

Collision detection calculation of robot to insert needles for interventional radiology 査読

Takayuki Matsuno, Kamegawa Tetsushi, Takao Hiraki

proc of 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) 2021年7月

Adaptive evolution strategy sample consensus for 3D reconstruction from two cameras 査読

Yuichiro Toda, Hsu Horng Yz, Takayuki Matsuno, Mamoru Minami, Dalin Zhou

25 ( 3 ) 466 - 474 2020年8月

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論文 - 松野　隆幸