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119 results on '"Sase, Kazuya"'

1. Measurement and Interpolation for Data-Driven Pressure Distribution Rendering on a Finger Pad

Author

Sase, Kazuya, Onodera, Rei, Nagano, Hikaru, and Konyo, Masashi

Subjects
Computer Science - Human-Computer Interaction
Abstract

We propose a data-driven pressure distribution rendering method that uses the interpolation of experimentally obtained pressure values. The pressure data were collected using a pressure sensor array. The prediction was performed using linear interpolation, assuming that the pressure distribution is dependent on pushing displacement and contact angle. Leap Motion Controller was used to implement the prediction based on user input. The proposed prediction model was found to be fast and reproduce the measured data well., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

Published
2024

2. Validation and motion analyses of laparoscopic radical nephrectomy with Thiel-embalmed cadavers

Author

Yan, Lingbo, Ebina, Koki, Abe, Takashige, Kon, Masafumi, Higuchi, Madoka, Hotta, Kiyohiko, Furumido, Jun, Iwahara, Naoya, Komizunai, Shunsuke, Tsujita, Teppei, Sase, Kazuya, Chen, Xiaoshuai, Kurashima, Yo, Kikuchi, Hiroshi, Miyata, Haruka, Matsumoto, Ryuji, Osawa, Takahiro, Murai, Sachiyo, Shichinohe, Toshiaki, Murakami, Soichi, Senoo, Taku, Watanabe, Masahiko, Konno, Atsushi, and Shinohara, Nobuo

Published
2024
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4. Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning

Author

Ebina, Koki, Abe, Takashige, Hotta, Kiyohiko, Higuchi, Madoka, Furumido, Jun, Iwahara, Naoya, Kon, Masafumi, Miyaji, Kou, Shibuya, Sayaka, Lingbo, Yan, Komizunai, Shunsuke, Kurashima, Yo, Kikuchi, Hiroshi, Matsumoto, Ryuji, Osawa, Takahiro, Murai, Sachiyo, Tsujita, Teppei, Sase, Kazuya, Chen, Xiaoshuai, Konno, Atsushi, and Shinohara, Nobuo

Published
2022
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5. Surgical Skill Analysis Based on the Way of Grasping Organs with Forceps in Dissection Procedure of Laparoscopic Surgery

Author

Ebina, Koki, Abe, Takashige, Komizunai, Shunsuke, Tsujita, Teppei, Sase, Kazuya, Chen, Xiaoshuai, Higuchi, Madoka, Furumido, Jun, Iwahara, Naoya, Kurashima, Yo, Shinohara, Nobuo, Konno, Atsushi, Serafini, Paolo, Managing Editor, Guazzelli, Elisabeth, Series Editor, Rammerstorfer, Franz G., Series Editor, Wall, Wolfgang A., Series Editor, Schrefler, Bernhard, Series Editor, Venture, Gentiane, editor, Solis, Jorge, editor, Takeda, Yukio, editor, and Konno, Atsushi, editor

Published
2021
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7. Development of a System for Determining Technique Level of Vascular Anastomosis Using Hand Motion.

Author

Chen, Xiaoshuai, Shoji, Taro, Kowatari, Ryosuke, Ebina, Koki, Kobayashi, Yoshifumi, Kato, Moeki, Kabasawa, Hinaha, Suzuki, Taisei, Sase, Kazuya, Tsujita, Teppei, Komizunai, Shunsuke, Oka, Kazuhiko, and Konno, Atsushi

Subjects
CORONARY artery bypass, SURGICAL anastomosis, CARDIOVASCULAR surgery, OPERATIVE surgery, SURGICAL education
Abstract

In off-pump coronary artery bypass grafting (OPCAB), the coronary arteries are joined as the heart beats. This procedure requires high skill and experience to be performed reliably and quickly. Although training kits are commonly used for technical training, the inability of trainees to always be with experienced surgeons for guidance and to receive immediate evaluation remains problematic. To address this problem, a system that allows a single trainee to observe and quantitatively evaluate the procedures performed by an experienced surgeon is being developed. In this study, to analyze the differences between the motions of experienced and novice surgeons, Leap Motion was used to measure the hand motion of the vascular anastomosis performed by both surgeons using a training kit. Using the measured data, the features of the vascular anastomosis surgical techniques performed by experienced and novices were tested using the Mann–Whitney U test. In addition, a binary classification was performed using machine learning with these features. The binary classification results show that trainees can be classified as experts or novices with high accuracy using the developed system. [ABSTRACT FROM AUTHOR]

Published
2024
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8. A surgical instrument motion measurement system for skill evaluation in practical laparoscopic surgery training.

Author

Ebina, Koki, Abe, Takashige, Yan, Lingbo, Hotta, Kiyohiko, Shichinohe, Toshiaki, Higuchi, Madoka, Iwahara, Naoya, Hosaka, Yukino, Harada, Shigeru, Kikuchi, Hiroshi, Miyata, Haruka, Matsumoto, Ryuji, Osawa, Takahiro, Kurashima, Yo, Watanabe, Masahiko, Kon, Masafumi, Murai, Sachiyo, Komizunai, Shunsuke, Tsujita, Teppei, and Sase, Kazuya

Subjects
SURGICAL instruments, LAPAROSCOPIC surgery, MOTION capture (Human mechanics), SURGICAL education, OPERATIVE surgery, MEASURING instruments
Abstract

This study developed and validated a surgical instrument motion measurement system for skill evaluation during practical laparoscopic surgery training. Owing to the various advantages of laparoscopic surgery including minimal invasiveness, this technique has been widely used. However, expert surgeons have insufficient time for providing training to beginners due to the shortage of surgeons and limited working hours. Skill transfer efficiency has to be improved for which there is an urgent need to develop objective surgical skill evaluation methods. Therefore, a simple motion capture–based surgical instrument motion measurement system that could be easily installed in an operating room for skill assessment during practical surgical training was developed. The tip positions and orientations of the instruments were calculated based on the marker positions attached to the root of the instrument. Because the patterns of these markers are individual, this system can track multiple instruments simultaneously and detect exchanges. However due to the many obstacles in the operating room, the measurement data included noise and outliers. In this study, the effect of this decrease in measurement accuracy on feature calculation was determined. Accuracy verification experiments were conducted during wet-lab training to demonstrate the capability of this system to measure the motion of surgical instruments with practical accuracy. A surgical training experiment on a cadaver was conducted, and the motions of six surgical instruments were measured in 36 cases of laparoscopic radical nephrectomy. Outlier removal and smoothing methods were also developed and applied to remove the noise and outliers in the obtained data. The questionnaire survey conducted during the experiment confirmed that the measurement system did not interfere with the surgical operation. Thus, the proposed system was capable of making reliable measurements with minimal impact on surgery. The system will facilitate surgical education by enabling the evaluation of skill transfer of surgical skills. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Surgical Skill Analysis Based on the Way of Grasping Organs with Forceps in Dissection Procedure of Laparoscopic Surgery

Author

Ebina, Koki, primary, Abe, Takashige, additional, Komizunai, Shunsuke, additional, Tsujita, Teppei, additional, Sase, Kazuya, additional, Chen, Xiaoshuai, additional, Higuchi, Madoka, additional, Furumido, Jun, additional, Iwahara, Naoya, additional, Kurashima, Yo, additional, Shinohara, Nobuo, additional, and Konno, Atsushi, additional

Published
2020
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10. Embedding Segmented Volume in Finite Element Mesh with Topology Preservation

Author

Sase, Kazuya, Tsujita, Teppei, Konno, Atsushi, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ourselin, Sebastien, editor, Joskowicz, Leo, editor, Sabuncu, Mert R., editor, Unal, Gozde, editor, and Wells, William, editor

Published
2016
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11. Moving Particle Semi-Implicit and Finite Element Method Coupled Analysis for Brain Shift Estimation

Author

Ema, Akito, Chen, Xiaoshuai, Sase, Kazuya, Tsujita, Teppei, and Konno, Atsushi

Subjects
brain shift, FEM, fluid-structure interaction (FSI), moving particle semi-implicit method (MPS)
Abstract

Neuronavigation is a computer-assisted technique for presenting three-dimensional images of a patient's brain to facilitate immediate and precise lesion localization by surgeons. Neuronavigation systems use preoperative medical images of patients. In neurosurgery, when the dura mater and arachnoid membrane are incised and the cerebrospinal fluid (CSF) drains out, the brain loses the CSF buoyancy and deforms in the direction of gravity, which is referred to as brain shift. This brain shift yields inaccurate neuronavigation. To reduce this inaccuracy, an intraoperative brain shift should be estimated. This paper proposes a dynamic simulation method for brain-shift estimation combining the moving-particle semi-implicit (MPS) method and the finite element method (FEM). The CSF was modeled using fluid particles, whereas the brain parenchyma was modeled using finite elements (FEs). Node particles were attached to the surface nodes of the brain parenchyma in the FE model. The interaction between the CSF and brain parenchyma was simulated using the repulsive force between the fluid particles and node particles. Validation experiments were performed using a gelatin block. The gelatin block was dipped into silicone oil, which was then gradually removed; the block deformation owing to the buoyancy loss was measured. The experimental deformation data were compared with the results of the MPS-FEM coupled analysis. The mean absolute error (MAE) between the simulated deformation and the average across the four experiments was 0.26 mm, while the mean absolute percentage error (MAPE) was 27.7%. Brain-shift simulations were performed using the MPS-FEM coupled analysis, and the computational cost was evaluated.

Published
2022

12. Basic Experiments Toward Mixed Reality Dynamic Navigation for Laparoscopic Surgery

Author

Chen, Xiaoshuai, Sakai, Daisuke, Fukuoka, Hiroaki, Shirai, Ryosuke, Ebina, Koki, Shibuya, Sayaka, Sase, Kazuya, Tsujita, Teppei, Abe, Takashige, Oka, Kazuhiko, and Konno, Atsushi

Subjects
soft object deformation, projection mapping, mixed reality (MR), computer-assisted surgery, laparoscopic surgery
Abstract

Laparoscopic surgery is a minimally invasive procedure that is performed by viewing endoscopic camera images. However, the limited field of view of endoscopic cameras makes laparoscopic surgery difficult. To provide more visual information during laparoscopic surgeries, augmented reality (AR) surgical navigation systems have been developed to visualize the positional relationship between the surgical field and organs based on preoperative medical images of a patient. However, since earlier studies used preoperative medical images, the navigation became inaccurate as the surgery progressed because the organs were displaced and deformed during surgery. To solve this problem, we propose a mixed reality (MR) surgery navigation system in which surgical instruments are tracked by a motion capture (Mocap) system; we also evaluated the contact between the instruments and organs and simulated and visualized the deformation of the organ caused by the contact. This paper describes a method for the numerical calculation of the deformation of a soft body. Then, the basic technology of MR and projection mapping is presented for MR surgical navigation. The accuracy of the simulated and visualized deformations is evaluated through basic experiments using a soft rectangular cuboid object.

Published
2022

14. Correction to: Motion analysis for better understanding of psychomotor skills in laparoscopy: objective assessment-based simulation training using animal organs

Author

Ebina, Koki, Abe, Takashige, Higuchi, Madoka, Furumido, Jun, Iwahara, Naoya, Kon, Masafumi, Hotta, Kiyohiko, Komizunai, Shunsuke, Kurashima, Yo, Kikuchi, Hiroshi, Matsumoto, Ryuji, Osawa, Takahiro, Murai, Sachiyo, Tsujita, Teppei, Sase, Kazuya, Chen, Xiaoshuai, Konno, Atsushi, and Shinohara, Nobuo

Published
2021
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15. Development and validation of a measurement system for laparoscopic surgical procedures in practical surgery training

Author

Ebina, Koki, primary, Abe, Takashige, additional, Hotta, Kiyohiko, additional, Higuchi, Madoka, additional, Furumido, Jun, additional, Iwahara, Naoya, additional, Kon, Masafumi, additional, Komizunai, Shunsuke, additional, Kurashima, Yo, additional, Kikuchi, Hiroshi, additional, Matsumoto, Ryuji, additional, Osawa, Takahiro, additional, Murai, Sachiyo, additional, Tsujita, Teppei, additional, Sase, Kazuya, additional, Chen, Xiaoshuai, additional, Shinohara, Nobuo, additional, and Konno, Atsushi, additional

Published
2023
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18. Automatic assessment of laparoscopic surgical skill competence based on motion metrics

Author

Ebina, Koki, primary, Abe, Takashige, additional, Hotta, Kiyohiko, additional, Higuchi, Madoka, additional, Furumido, Jun, additional, Iwahara, Naoya, additional, Kon, Masafumi, additional, Miyaji, Kou, additional, Shibuya, Sayaka, additional, Lingbo, Yan, additional, Komizunai, Shunsuke, additional, Kurashima, Yo, additional, Kikuchi, Hiroshi, additional, Matsumoto, Ryuji, additional, Osawa, Takahiro, additional, Murai, Sachiyo, additional, Tsujita, Teppei, additional, Sase, Kazuya, additional, Chen, Xiaoshuai, additional, Konno, Atsushi, additional, and Shinohara, Nobuo, additional

Published
2022
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19. Numerical Calculation Method for Brain Shift Based on Hydrostatics and Dynamic FEM

Author

Chen, Xiaoshuai, Shirai, Ryosuke, Masamune, Ken, Tamura, Manabu, Muragaki, Yoshihiro, Sase, Kazuya, Tsujita, Teppei, 1000090250688, Konno, Atsushi, Chen, Xiaoshuai, Shirai, Ryosuke, Masamune, Ken, Tamura, Manabu, Muragaki, Yoshihiro, Sase, Kazuya, Tsujita, Teppei, 1000090250688, and Konno, Atsushi

Abstract

During neurosurgery, brain deformation occurs because of gravity and leakage of the cerebrospinal fluid (CSF), which is referred to as brain shift. Brain shift is a serious problem in neuronavigation because neuronavigation relies on preoperatively taken medical images. This paper presents a brain shift estimation method based on hydrostatics and dynamic FEM, assuming that gravity and leakage of CSF are the main reasons for brain shift. The accuracy of the proposed method was verified via basic experiments conducted using elastic gelatin cubes. In addition, a 3D brain model was created using preoperative medical images of a patient and brain shift estimation simulations were performed. Their accuracy was verified by comparing the simulation results with the actual brain shift during neurosurgery. Assuming that the node in the most anterior position of the frontal lobe and the node in the highest position of the parietal lobe before the brain shift respectively remain in the most anterior position and the highest position even after the brain shift, the corresponding regions before and after the brain shift were searched and the deformations were evaluated. In this error analysis, the maximum estimation error was 4.4 mm. Furthermore, a region of 40 mm x 30 mm in the frontal lobe was chosen as the region of interest (ROI), and the surface errors in the ROI between the intraoperative MRI images and the simulated shifted brain were analyzed. The mean absolute error (MAE) between the surfaces along the z-axis (the direction of gravity) in the ROI was 3.7 mm (maximum absolute error was 8.8 mm). The proposed method was sufficiently simple for computing the brain shift in real-time. The expected contribution of this study toward improving the neuronavigational error and enhancing the safety of neurosurgery will be beneficial for hospitals, especially when the intraoperative MRI cannot be performed.

Published
2022

20. Automatic assessment of laparoscopic surgical skill competence based on motion metrics

Author

Ebina, Koki, Abe, Takashige, Hotta, Kiyohiko, Higuchi, Madoka, Furumido, Jun, Iwahara, Naoya, Kon, Masafumi, Miyaji, Kou, Shibuya, Sayaka, Lingbo, Yan, Komizunai, Shunsuke, Kurashima, Yo, Kikuchi, Hiroshi, Matsumoto, Ryuji, Osawa, Takahiro, Murai, Sachiyo, Tsujita, Teppei, Sase, Kazuya, Chen, Xiaoshuai, Konno, Atsushi, 1000090250422, Shinohara, Nobuo, Ebina, Koki, Abe, Takashige, Hotta, Kiyohiko, Higuchi, Madoka, Furumido, Jun, Iwahara, Naoya, Kon, Masafumi, Miyaji, Kou, Shibuya, Sayaka, Lingbo, Yan, Komizunai, Shunsuke, Kurashima, Yo, Kikuchi, Hiroshi, Matsumoto, Ryuji, Osawa, Takahiro, Murai, Sachiyo, Tsujita, Teppei, Sase, Kazuya, Chen, Xiaoshuai, Konno, Atsushi, 1000090250422, and Shinohara, Nobuo

Abstract

The purpose of this study was to characterize the motion features of surgical devices associated with laparoscopic surgical competency and build an automatic skill-credential system in porcine cadaver organ simulation training. Participants performed tissue dissection around the aorta, dividing vascular pedicles after applying Hem-o-lok (tissue dissection task) and parenchymal closure of the kidney (suturing task). Movements of surgical devices were tracked by a motion capture (Mocap) system, and Mocap-metrics were compared according to the level of surgical experience (experts: >= 50 laparoscopic surgeries, intermediates: 10-49, novices: 0-9), using the Kruskal-Wallis test and principal component analysis (PCA). Three machine-learning algorithms: support vector machine (SVM), PCA-SVM, and gradient boosting decision tree (GBDT), were utilized for discrimination of the surgical experience level. The accuracy of each model was evaluated by nested and repeated k-fold cross-validation. A total of 32 experts, 18 intermediates, and 20 novices participated in the present study. PCA revealed that efficiency-related metrics (e.g., path length) significantly contributed to PC 1 in both tasks. Regarding PC 2, speed-related metrics (e.g., velocity, acceleration, jerk) of right-hand devices largely contributed to the tissue dissection task, while those of left-hand devices did in the suturing task. Regarding the three-group discrimination, in the tissue dissection task, the GBDT method was superior to the other methods (median accuracy: 68.6%). In the suturing task, SVM and PCA-SVM methods were superior to the GBDT method (57.4 and 58.4%, respectively). Regarding the two-group discrimination (experts vs. intermediates/novices), the GBDT method resulted in a median accuracy of 72.9% in the tissue dissection task, and, in the suturing task, the PCA-SVM method resulted in a median accuracy of 69.2%. Overall, the mocap-based credential system using machine-learning classifiers pr

Published
2022

23. MP41-04 MOTION METRICS AND MACHINE LEARNING-BASED FEEDBACK IN WET-LAB LAPAROSCOPIC SURGICAL TRAINING

Author

Abe, Takashige, primary, Ebina, Koki, additional, Hotta, Kiyohiko, additional, Higuchi, Madoka, additional, Furumido, Jun, additional, Iwahara, Naoya, additional, Kon, Masafumi, additional, Miyaji, Kou, additional, Shibuya, Sayaka, additional, Lingbo, Yan, additional, Komizunai, Suhnsuke, additional, Kurashima, Yo, additional, Kikuchi, Hiroshi, additional, Matsumoto, Ryuji, additional, Murai, Sachiyo, additional, Tsujita, Teppei, additional, Sase, Kazuya, additional, Chen, Xiaoshuai, additional, Konno, Atsushi, additional, and Shinohara, Nobuo, additional

Published
2022
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32. Correction to: Motion analysis for better understanding of psychomotor skills in laparoscopy: objective assessment-based simulation training using animal organs

Author

Ebina, Koki, primary, Abe, Takashige, additional, Higuchi, Madoka, additional, Furumido, Jun, additional, Iwahara, Naoya, additional, Kon, Masafumi, additional, Hotta, Kiyohiko, additional, Komizunai, Shunsuke, additional, Kurashima, Yo, additional, Kikuchi, Hiroshi, additional, Matsumoto, Ryuji, additional, Osawa, Takahiro, additional, Murai, Sachiyo, additional, Tsujita, Teppei, additional, Sase, Kazuya, additional, Chen, Xiaoshuai, additional, Konno, Atsushi, additional, and Shinohara, Nobuo, additional

Published
2020
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33. Motion analysis for better understanding of psychomotor skills in laparoscopy: objective assessment-based simulation training using animal organs

Author

Ebina, Koki, primary, Abe, Takashige, additional, Higuchi, Madoka, additional, Furumido, Jun, additional, Iwahara, Naoya, additional, Kon, Masafumi, additional, Hotta, Kiyohiko, additional, Komizunai, Shunsuke, additional, Kurashima, Yo, additional, Kikuchi, Hiroshi, additional, Matsumoto, Ryuji, additional, Osawa, Takahiro, additional, Murai, Sachiyo, additional, Tsujita, Teppei, additional, Sase, Kazuya, additional, Chen, Xiaoshuai, additional, Konno, Atsushi, additional, and Shinohara, Nobuo, additional

Published
2020
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42. A Measurement System for Skill Evaluation of Laparoscopic Surgical Procedures

Author

Ebina, Koki, primary, Abe, Takashige, additional, Komizunai, Shunsuke, additional, Tsujita, Teppei, additional, Sase, Kazuya, additional, Chen, Xiaoshuai, additional, Higuchi, Madoka, additional, Furumido, Jun, additional, Iwahara, Naoya, additional, Kurashima, Yo, additional, Shinohara, Nobuo, additional, and Konno, Atsushi, additional

Published
2019
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