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IEEE International Conference on Systems, Man and Cybernetics

作者： Rafael Anicet Zanini Esther Luna Colombini Maria Claudia Ferrari de Castro Laboratory of Robotics and Cognitive Science (LaRoCS) UNICAMP Campinas Brazil Department of Computer Sciente Fundao Educacional Inaciana Padre Sabia de Medeiros (FEI) Sao Bernardo do Campo Brazil

This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, and provide reasonable predictions for both EMG envelopes and EMG raw signals. Therefore, one could use these models as input for a control strategy for functional electrical stimulation (FES) devices used on tremor suppression, by dynamically predicting and improving FES control parameters based on tremor forecast.

关键词： Electromyography Iron Predictive models Recurrent neural networks Neurons Correlation Adaptation models

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In-transit metastatic lymph nodes in cervical cancer: A new staging and therapeutic concept

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European Journal of Obstetrics & Gynecology and Reproductive Biology 2025年 312卷 114523页

作者： Matteo Pavone Nicolò Bizzarri Agnieszka Rychlik Jan Persson Anna Fagotti Francesco Fanfani Giovanni Scambia Denis Querleu UOC Ginecologia Oncologica Dipartimento di Scienze per la salute della Donna e del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy IHU Strasbourg Institute of Image-Guided surgery Strasbourg France ICube Laboratory of Engineering Computer Science and Imaging Department of Robotics Imaging Teledetection and Healthcare Technologies University of Strasbourg CNRS UMR 7357 Strasbourg France Research Institute against Digestive Cancer IRCAD Strasbourg France Department of Gynecologic Oncology National Research Institute of Oncology Warsaw Poland Department of Obstetrics and Gynaecology Skåne University Hospital Lund University Faculty of Medicine Clinical Sciencies Obstetrics and Gynecology Lund Sweden Università Cattolica del Sacro Cuore Rome Italy

Objective It has been reported that metastatic lymph nodes can be present in the parauterine lymphovascular (PULT) and in lateral paracervical lymphatic tissue as in-transit disease in patients with cervical cancer. This study aimed to clarify the understanding of in-transit positive lymph nodes in PULT and lateral paracervix and its clinical management in patients with cervical cancer, based on insights gathered from an international expert survey. Methods A 29-question survey was emailed to experts identified through a systematic literature search related to sentinel lymph nodes in gynecological cancer. The survey was divided into four sections: 1. Resection of parauterine lymphovascular tissue; 2. Discontinuous lymphatic paracervical involvement; 3. Positive para -uterine or paracervical nodes in fertility-sparing surgery; 4. The impact of positive in-transit nodes on the extent of nodal staging. Consensus was defined as an agreement among at least 70% of the respondents. Results Responders where only corresponding or last authors of articles addressing sentinel lymph nodes procedures. Results show general agreement on referring to the areas as “ para -uterine lymphovascular tissue (PULT)” (84.85 %), with lymph nodes in these regions considered sentinel nodes if stained with indocyanine green (80 %). However, there is no consensus on surgical staging for isolated positive lymph nodes. Most respondents favour adjuvant treatment for in-transit macro or micrometastases (>95 %) but acknowledge the lack of conclusive evidence. In fertility-sparing settings, most would remove PULT while preserving uterine arteries (59 %) and would remove lateral paracervical tissue in low-risk cases (66.7 %). For isolated metastasis, most would recommend chemoradiation over fertility-sparing options (81.8 %). Finally, if a positive in-transit node is found, experts would request additional imaging (80.3 %), though there is no consensus on aortic lymphatic dissection or extended-field r

关键词： Parauterine lymphovascular tissue Intransit nodes Lateral paracervical lymphatic tissue Sentinel lymph node Cervical cancer

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Ultrasound thermometry using an ultrasound element and deep learning for HIFU

Ultrasound thermometry using an ultrasound element and deep ...

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IEEE International Ultrasonics Symposium

作者： Changfan Chen Younsu Kim Chloe Audigier Emad M. Boctor Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore Maryland USA Department of Computer Science Johns Hopkins University Baltimore Maryland USA Department of Radiology Johns Hopkins University Baltimore Maryland USA

High intensity focused ultrasound (HIFU) is a non-invasive therapy used to induce thermal dose to target tissue for desired medical outcomes. With this technique, malignant tissues can be destroyed using high energy with minimal side effects compared to surgery, that can induce more pain and leave permanent scar to patients. Temperature monitoring is crucial to preserve healthy tissues during thermal therapies. Magnetic resonance (MR) thermometry is often used to monitor the ablation process precisely. However, it is costly and some patients have contraindications. Ultrasound is a cost-effective medical imaging modality and suffers from less restrictions on the operating environment and can be used safely with all patients. In this paper, we propose a method to monitor the temperature with ultrasound using a deep learning approach. The system is designed to collect ultrasound channel data during HIFU therapy and alternates ablation phases and monitoring phases. In the monitoring phase, ultrasound elements in the probe receive ultrasound pulses sent from the 256 HIFU elements sequentially. We use convolutional long short term memory (ConvLSTM) neural network to generate temperature images from the ultrasound channel data. The temperature images are compared with the ones collected from MR thermometry. Mean and max difference of each image are calculated to evaluate the performance of designed neural network. We achieve 0.57 ± 0.33℃ of mean difference and 1.99 ± 1.07℃ of max difference in axial plane. In coronal plane, we achieve 0.33 ± 0.19℃ of mean difference and 1.54 ± 1.04℃ of max difference. The results show the potential use of ultrasound and deep learning to reconstruct temperature images.

关键词： Ultrasonic imaging Monitoring Machine learning Temperature measurement Temperature sensors Probes Neural networks

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Corrigendum to "PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images" [Medical Image Analysis 99 (2025),103353]

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Medical image analysis 2025年 104卷 103651页

作者： Jieyun Bai Zihao Zhou Zhanhong Ou Gregor Koehler Raphael Stock Klaus Maier-Hein Marawan Elbatel Robert Martí Xiaomeng Li Yaoyang Qiu Panjie Gou Gongping Chen Lei Zhao Jianxun Zhang Yu Dai Fangyijie Wang Guénolé Silvestre Kathleen Curran Hongkun Sun Jing Xu Pengzhou Cai Lu Jiang Libin Lan Dong Ni Mei Zhong Gaowen Chen Víctor M Campello Yaosheng Lu Karim Lekadir Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand. Electronic address: jbai996@aucklanduni.ac.nz. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science and Engineering The Chinese University of Hong Kong China. Computer Vision and Robotics Group University of Girona Girona Spain. Canon Medical Systems (China) Co. LTD Beijing China. College of Artificial Intelligence Nankai University Tianjin China. College of Computer Science and Electronic Engineering Hunan University Changsha China. School of Medicine University College Dublin Dublin Ireland. School of Computer Science University College Dublin Dublin Ireland. School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China. School of Computer Science & Engineering Chongqing University of Technology Chongqing China. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound & Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging & School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China. NanFang Hospital of Southern Medical University Guangzhou China. Zhujiang Hospital of Southern Medical University Guangzhou China. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Electronic address: tluys@***. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain.

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Agile reactive navigation for a non-holonomic mobile robot using a pixel processor array

arXiv

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arXiv 2020年

作者： Liu, Yanan Bose, Laurie Greatwood, Colin Chen, Jianing Fan, Rui Richardson, Thomas Carey, Stephen J. Dudek, Piotr Mayol-Cuevas, Walterio Bristol Robotics Laboratory University of Bristol BristolBS16 1QY United Kingdom Visual Information Laboratory University of Bristol BristolBS1 5DD United Kingdom Department of Electrical and Electronic Engineering University of Manchester ManchesterM13 9PL United Kingdom Department of Computer Science and Engineering Department of Ophthalmology University of California San DiegoCA92093 United States Hangzhou ATG Intelligent Technology Co. Ltd. Hangzhou China Amazon Seattle United States

This paper presents an agile reactive navigation strategy for driving a non-holonomic ground vehicle around a preset course of gates in a cluttered environment using a low-cost processor array sensor. This enables machine vision tasks to be performed directly upon the sensor’s image plane, rather than using a separate general-purpose computer. We demonstrate a small ground vehicle running through or avoiding multiple gates at high speed using minimal computational resources. To achieve this, target tracking algorithms are developed for the Pixel Processing Array and captured images are then processed directly on the vision sensor acquiring target information for controlling the ground vehicle. The algorithm can run at up to 2000 fps outdoors and 200fps at indoor illumination levels. Conducting image processing at the sensor level avoids the bottleneck of image transfer encountered in conventional sensors. The real-time performance of on-board image processing and robustness is validated through experiments. Experimental results demonstrate that the algorithm’s ability to enable a ground vehicle to navigate at an average speed of 2.20 m/s for passing through multiple gates and 3.88 m/s for a ‘slalom’ task in an environment featuring significant visual clutter. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

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A biomechanical study on the use of curved drilling technique for treatment of osteonecrosis of femoral head

arXiv

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arXiv 2019年

作者： Bakhtiarinejad, Mahsan Alambeigi, Farshid Chamani, Alireza Unberath, Mathias Khanuja, Harpal Armand, Mehran Laboratory for Computational Sensing and Robotics Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Orthopedic Surgery Johns Hopkins Medical School BaltimoreMD21205 United States Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States

Osteonecrosis occurs due to the loss of blood supply to the bone, leading to spontaneous death of the trabecular bone. Delayed treatment of the involved patients results in collapse of the femoral head, which leads to a need for total hip arthroplasty surgery. Core decompression, as the most popular technique for treatment of the osteonecrosis, includes removal of the lesion area by drilling a straight tunnel to the lesion, debriding the dead bone and replacing it with bone substitutes. However, there are two drawbacks for this treatment method. First, due to the rigidity of the instruments currently used during core decompression, lesions cannot be completely removed and/or excessive healthy bone may also be removed with the lesion. Second, the use of bone substitutes, despite its biocompatibility and osteoconductivity, may not provide sufficient mechanical strength and support for the bone. To address these shortcomings, a novel robot-assisted curved core decompression (CCD) technique is introduced to provide surgeons with direct access to the lesions causing minimal damage to the healthy bone. In this study, with the aid of finite element (FE) simulations, we investigate biomechanical performance of core decompression using the curved drilling technique in the presence of normal gait loading. In this regard, we compare the result of the CCD using bone substitutes and flexible implants with other conventional core decompression techniques. The study finding shows that the maximum principal stress occurring at the superior domain of the neck is smaller in the CCD techniques (i.e. 52.847 MPa) compared to the other core decompression methods;furthermore, the peak value of normal stress at the interface for the CCD model is substantially smaller than traditional and advanced core decompression techniques (89% and 76%, respectively). FE results demonstrate the superior performance of CCD compared to the other approaches without any compromise to patient’s safety and ma

关键词： Finite element method

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Cooperative Intelligence for Autonomous Driving

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ZTE Communications 2019年第2期17卷 44-50页

作者： CHENG Xiang DUAN Dongliang YANG Liuqing ZHENG Nanning State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Department of Electrical and Computer Engineering University of WyomingLaramieWY 82071USA Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’anShaanxi 710049China

Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

关键词： autonomous driving cooperative intelligence information fusion vehicular communications and networking

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Image-Guided Robotic Surgery For Sentinel Lymph Node Status Assessment In Uterine Cancers Using Ultrasound Drop-In Probe: Surgical Technique In 10 Steps

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Matteo Pavone Elena Teodorico Nicolò Bizzarri Chiara Innocenzi Andrea Rosati Michela Zorzi Chiara Cantarini Marianna Ciancia Francesca Moro Valerio Gallotta Jacques Marescaux Lise Lecointre Anna Fagotti Giovanni Scambia Francesco Fanfani Antonia Carla Testa Denis Querleu UOC Ginecologia Oncologica Dipartimento di Scienze per la salute della Donna e del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Research Institute against Digestive Cancer IRCAD Strasbourg Strasbourg France ICube Laboratory of Engineering Computer Science and Imaging Department of Robotics Imaging Teledetection and Healthcare Technologies University of Strasbourg CNRS UMR 7357 Strasbourg France University Hospitals of Strasbourg Department of Gynecologic Surgery Strasbourg Italy UOC Ginecologia Oncologica Dipartimento di Scienze per la salute della Donna e del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Roma Italy

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Correction to: Editorial

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International Journal of Social robotics 2020年第1期12卷 297-297页

作者： Agnieszka Wykowska Shuzhi Sam Ge Oussama Khatib Social Cognition in Human-Robot Interaction Istituto Italiano Di Tecnologia Genoa Italy Social Robotics Laboratory Interactive Digital Media Institute The National University of Singapore Singapore Singapore Centre for Robotics UESTC Chengdu China Robotics Laboratory Department of Computer Science Stanford University Stanford USA

The following mistakes were inadvertently overlooked in the production of the Editorial of the January issue of Social robotics.

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Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT

arXiv

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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： computerized tomography

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