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J. Medical Robotics Res.最新文献

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Calibration and Evaluation of a Motion Measurement System for PET Imaging Studies 用于PET成像研究的运动测量系统的校准和评估
Pub Date : 2023-05-05 DOI: 10.1142/s2424905x23400032
Junxiang Wang, Ti Wu, I. Iordachita, P. Kazanzides
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引用次数: 1
Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels 基于自监督学习和最小标签的手术视频出体帧隐私保护自动检测
Pub Date : 2023-03-31 DOI: 10.48550/arXiv.2303.18106
Ziheng Wang, Conor Perreault, Xi Liu, A. Jarc
Endoscopic video recordings are widely used in minimally invasive robot-assisted surgery, but when the endoscope is outside the patient's body, it can capture irrelevant segments that may contain sensitive information. To address this, we propose a framework that accurately detects out-of-body frames in surgical videos by leveraging self-supervision with minimal data labels. We use a massive amount of unlabeled endoscopic images to learn meaningful representations in a self-supervised manner. Our approach, which involves pre-training on an auxiliary task and fine-tuning with limited supervision, outperforms previous methods for detecting out-of-body frames in surgical videos captured from da Vinci X and Xi surgical systems. The average F1 scores range from 96.00 to 98.02. Remarkably, using only 5% of the training labels, our approach still maintains an average F1 score performance above 97, outperforming fully-supervised methods with 95% fewer labels. These results demonstrate the potential of our framework to facilitate the safe handling of surgical video recordings and enhance data privacy protection in minimally invasive surgery.
内窥镜录像广泛应用于微创机器人辅助手术,但当内窥镜在患者体外时,它可能会捕捉到可能包含敏感信息的不相关片段。为了解决这个问题,我们提出了一个框架,通过利用最小数据标签的自我监督来准确检测手术视频中的体外帧。我们使用大量未标记的内窥镜图像以自我监督的方式学习有意义的表示。F1的平均分数在96.00到98.02之间。值得注意的是,仅使用5%的训练标签,我们的方法仍然保持平均F1分数高于97,优于标签减少95%的完全监督方法。这些结果证明了我们的框架在促进手术视频记录的安全处理和加强微创手术数据隐私保护方面的潜力。
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引用次数: 0
A Novel Mechanical Design of a Wearable Fingertip Haptic Device for Remote Meniscus Palpation 一种用于半月板远程触诊的可穿戴指尖触觉装置的新型机械设计
Pub Date : 2023-03-17 DOI: 10.1142/s2424905x23500010
Samir Morad, Zainab Jaffer, S. Dogramadzi
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引用次数: 0
A Surgical Robotic Framework for Safe and Autonomous Data-Driven Learning and Manipulation of an Unknown Deformable Tissue with an Integrated Critical Space 一种用于安全自主数据驱动学习和操作具有集成临界空间的未知变形组织的外科机器人框架
Pub Date : 2023-01-19 DOI: 10.1142/s2424905x23400019
Braden P. Murphy, F. Alambeigi
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引用次数: 2
Development and Testing of a Durable and Novel Breast Phantom for Robotic Autonomous Ultrasound Systems 一种用于机器人自主超声系统的新型耐用乳房模体的研制与测试
Pub Date : 2022-11-16 DOI: 10.1142/s2424905x22410100
Siobhan R. Oca, Amy Strong, Jiselle Havas, Daniel M. Buckland, L. Bridgeman
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引用次数: 1
Experimental Evaluation of a 3-Armed 6-DOF Parallel Robot for Femur Fracture Surgery 三臂六自由度并联机器人在股骨骨折手术中的实验评价
Pub Date : 2022-10-28 DOI: 10.1142/s2424905x22410094
Fayez Alruwaili, Marzieh S. Saeedi-Hosseiny, M. Clancy, S. McMillan, I. Iordachita, Mohammad H. Abedin-Nasab
{"title":"Experimental Evaluation of a 3-Armed 6-DOF Parallel Robot for Femur Fracture Surgery","authors":"Fayez Alruwaili, Marzieh S. Saeedi-Hosseiny, M. Clancy, S. McMillan, I. Iordachita, Mohammad H. Abedin-Nasab","doi":"10.1142/s2424905x22410094","DOIUrl":"https://doi.org/10.1142/s2424905x22410094","url":null,"abstract":"","PeriodicalId":447761,"journal":{"name":"J. Medical Robotics Res.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126755146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Concentric Dual-Chamber Pneumatic Artificial Muscles: Miniature Actuators Designed for Use in Minimally Invasive Surgical Instruments 同心双腔气动人造肌肉:用于微创手术器械的微型致动器
Pub Date : 2022-08-26 DOI: 10.1142/s2424905x22410070
R. Lathrop, M. Ourak, J. Deprest, E. V. Poorten
{"title":"Concentric Dual-Chamber Pneumatic Artificial Muscles: Miniature Actuators Designed for Use in Minimally Invasive Surgical Instruments","authors":"R. Lathrop, M. Ourak, J. Deprest, E. V. Poorten","doi":"10.1142/s2424905x22410070","DOIUrl":"https://doi.org/10.1142/s2424905x22410070","url":null,"abstract":"","PeriodicalId":447761,"journal":{"name":"J. Medical Robotics Res.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121351968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Estimation of Needle Puncturing Form during Slight Needle Movement Based on Force Data for Robotic Automated Puncturing Function 基于机器人自动穿刺功能力数据的微小运动时穿刺形态估计
Pub Date : 2022-05-26 DOI: 10.1142/s2424905x22410045
T. Matsuno, Hikaru Murakami, T. Kamegawa, Nanako Sakai, T. Hiraki, Y. Toda
{"title":"Estimation of Needle Puncturing Form during Slight Needle Movement Based on Force Data for Robotic Automated Puncturing Function","authors":"T. Matsuno, Hikaru Murakami, T. Kamegawa, Nanako Sakai, T. Hiraki, Y. Toda","doi":"10.1142/s2424905x22410045","DOIUrl":"https://doi.org/10.1142/s2424905x22410045","url":null,"abstract":"","PeriodicalId":447761,"journal":{"name":"J. Medical Robotics Res.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115076172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design of a Flat Panel Detector for the PET-Laparoscope System Using Simulations 基于仿真的pet -腹腔镜系统平板探测器设计
Pub Date : 2022-04-27 DOI: 10.1142/s2424905x2241001x
Xiao Yue, M. R. Liyanaarachchi, K. Shimazoe, Kenichiro Ogane, H. Takahashi, M. Takahashi, Kazuaki Hara, E. Kobayashi, I. Sakuma
To improve spatial resolution in the PET-laparoscope system for intraoperative metastatic lymph nodes detection, design of a flat panel detector was optimized using Monte Carlo simulation tools. The performance of the new fixed detector was evaluated in point radio-source and phantom experiments. The experiment result shows that the system with the new fixed detector design has a spatial resolution better than 10[Formula: see text]mm in three dimensions, and the system can distinguish two radio-sources clearly in a 2-minute scan.
为了提高pet -腹腔镜系统术中转移淋巴结检测的空间分辨率,利用蒙特卡罗模拟工具对平板探测器的设计进行了优化。通过点辐射源实验和模体实验对新型固定探测器的性能进行了评价。实验结果表明,采用新型固定探测器设计的系统在三维空间分辨率优于10[公式:见文]mm,系统可以在2分钟的扫描时间内清晰地识别两个射源。
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引用次数: 0
Toward Automatic Detection of Gastric Lesion for Upper Gastrointestinal Endoscopy with Neural Network 基于神经网络的上消化道内镜胃病变自动检测研究
Pub Date : 2022-01-12 DOI: 10.1142/s2424905x21410038
Sai-yu Wang, Qi He, Ping Zhang, Xin Chen, Siyang Zuo
In this paper, we compared the performance of several neural networks in the classification of early gastric cancer (EGC) images and proposed a method of converting the output value of the network into a calorific value to locate the lesion. The algorithm was improved using transfer learning and fine-tuning principles. The test set accuracy rate reached 0.72, sensitivity reached 0.67, specificity reached 0.77, and precision rate reached 0.78. The experimental results show the potential to meet clinical demands for automatic detection of gastric lesion.
在本文中,我们比较了几种神经网络在早期胃癌(EGC)图像分类中的表现,并提出了一种将网络的输出值转换为热值来定位病变的方法。利用迁移学习和微调原理对算法进行了改进。检测集准确率为0.72,灵敏度为0.67,特异度为0.77,精密度为0.78。实验结果表明,该系统可以满足临床对胃病变自动检测的需求。
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引用次数: 0
期刊
J. Medical Robotics Res.
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