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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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Lower limb joint angular phase plane coordination analysis on standard maximum vertical jump

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Gait & Posture 2021年 90卷 205-206页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University CICANT Centre for Research in Applied Communication Culture and New Technologies- MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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A Wireless Near-Infrared Spectroscopy Device for Flap Monitoring: Proof of Concept in a Porcine Musculocutaneous Flap Model

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Journal of Reconstructive Microsurgery 2021年第2期38卷 096-105页

作者： Changsheng Wu Alina Y. Rwei Jong Yoon Lee Wei Ouyang Lauren Jacobson Haixu Shen Haiwen Luan Yameng Xu Jun Bin Park Sung Soo Kwak Xiaoyue Ni Wubin Bai Daniel Franklin Shuo Li Yiming Liu Xinchen Ni Amanda M. Westman Matthew R. MacEwan John A. Rogers Mitchell A. Pet Department of Materials Science and Engineering Querrey Simpson Institute for Bioelectronics Northwestern University Chicago Illinois Department of Chemical Engineering Delft University of Technology Delft The Netherlands Sibel Inc. Evanston Illinois Division of Plastic and Reconstructive Surgery Department of Surgery School of Medicine Washington University St. Louis Missouri Department of Materials Science and Engineering Northwestern University Evanston Illinois Department of Neurosurgery School of Medicine Washington University St. Louis Missouri Department of Electrical and Computer Engineering Northwestern University Evanston Illinois Department of Mechanical Engineering Northwestern University Evanston Illinois Department of Biomedical Engineering Northwestern University Evanston Illinois Department of Chemistry Northwestern University Evanston Illinois Department of Neurological Surgery Feinberg School of Medicine Northwestern University Chicago Illinois

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Whole-body phase plane analysis for standard maximum vertical jump assessment

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Gait & Posture 2021年 90卷 203-204页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University CICANT Centre for Research in Applied Communication Culture and New Technologies - MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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Wireless electronic tattoos 12

Wireless electronic tattoos

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12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019

作者： Lu, Nanshu Jeong, Hyoyoung Department of Aerospace Engineering and Engineering Mechanic Department of Biomedical Engineering Department of Electrical and Computer Engineering Texas Materials Institute University of Texas at Austin 2501 Speedway Austin78712 United States Department of Electrical and Computer Engineering University of Texas at Austin 2501 Speedway Austin78712 United States

ISBN: (纸本)9781605956015

Wearable electronics are finding emerging applications in mobile health, rehabilitation, prosthetics/exoskeletons, athletic training, human-machine interaction, etc. However, our skin is soft, curvilinear and dynamic whereas wafer-based electronics are hard, planar, and rigid. As a result, state-of-the-art wearables can only be strapped or clipped on human body. The development of flexible and stretchable electronics offers a remedy for such challenge. E-tattoos represent a class of stretchable circuits, sensors, and actuators that are ultrathin, ultrasoft, skin-conformable and deformable just like a temporary tattoo. Our group has invented a low-cost, dry and freeform "cut-solder-paste" method for the rapid prototyping of multilayer and reconfigurable wireless e-tattoos. This method has been proved to work for thin film metals, polymers, ceramics, 2D materials and their integration with rigid integrated circuits (ICs). Through a modular and multilayer design, we created e-tattoos that can wirelessly receive power and transmit data through near field communication (NFC) and Bluetooth. Such e-tattoos can conform to any part of human skin for electrophysiological, mechanical, thermal and optical sensing. © 2019 by DEStech Publications, Inc. All rights reserved.

关键词： Internet of things

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Multi-Agent Trust Region Policy Optimization

arXiv

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arXiv 2020年

作者： Li, Hepeng He, Haibo The Department of Electrical Computer and Biomedical Engineering University of Rhode Island South KingstownRI United States

We extend trust region policy optimization (TRPO) [1] to cooperative multi-agent reinforcement learning (MARL) for partially observable Markov games (POMG). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents’ observation is sufficient. By using local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratio with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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Phase-induced frequency conversion and doppler effect with time-modulated metasurfaces

arXiv

引用

arXiv 2021年

作者： Ramaccia, Davide Sounas, Dimitrios L. Alù, Andrea Toscano, Alessandro Bilotti, Filiberto Department of Engineering ROMA TRE University Via Vito Volterra 62 Rome00146 Italy Department of Electrical and Computer Engineering Wayne State University DetroitMI48202 United States Photonics Initiative Advanced Science Research Center Physics Program Graduate Center Department of Electrical Engineering City College City University of New York New YorkNY10031 United States

Metasurfaces consisting of electrically thin and densely packed planar arrays of subwavelength elements enable an unprecedented control of the impinging electromagnetic fields. Spatially modulated metasurfaces can efficiently tailor the spatial distribution of these fields with great flexibility. Similarly, time modulated metasurfaces can be successfully used to manipulate the frequency content and time variations of the impinging field. In this paper, we present time-modulated reflective metasurfaces that cause a frequency shift to the impinging radiation, thus realizing an artificial Doppler effect in a non-moving electrically thin structure. Starting from the theoretical analysis, we analytically derive the required time modulation of the surface admittance to achieve this effect, and present a realistic time-varying structure, based on a properly designed and dynamically tuned high-impedance surface. It is analytically and numerically demonstrated that the field emerging from the metasurface is up-/down-converted in frequency according to the modulation profile of the metasurface. The proposed metasurface concept, enabling a frequency modulation of the electromagnetic field "on-the-fly", may find application in telecommunication, radar, and sensing scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Doppler effect

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Reply to Grassi and Quaresima

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Journal of applied physiology (Bethesda, Md. : 1985) 2022年第1期132卷 222-223页

作者： Bhabuk Koirala Alessandro Concas Yi Sun L Bruce Gladden Nicola Lai Department of Electrical and Computer Engineering Old Dominion University Norfolk Virginia. Biomedical Engineering Institute Old Dominion University Norfolk Virginia. Center for Advanced Studies Research and Development in Sardinia Cagliari Italy. Department of Mechanical Chemical and Materials Engineering University of Cagliari Cagliari Italy. Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education East China Normal University Shanghai China. School of Physical Education & Health Care East China Normal University Shanghai China. School of Kinesiology Auburn University Auburn Alabama.

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Deep Learning Detection of Cardiac Akinesis in Echocardiograms 25th

Deep Learning Detection of Cardiac Akinesis in Echocardiogra...

引用

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Bitetto, Alessandro Bianchi, Elena Dondi, Piercarlo Bianchi, Luca Tolgyesi, Janos Ferri, Diego Lombardi, Luca Cerchiello, Paola Marceca, Azzurra Barosi, Alberto Department of Electrical Computer and Biomedical Engineering University of Pavia Via Ferrata 5 Pavia27100 Italy Neosperience SpA Via Gaspare Gozzi 1/A Milano20129 Italy Looptribe Srl Viale Italia 8 Brescia25126 Italy Department of Economics and Management University of Pavia Via San Felice 5 Pavia27100 Italy Department of Cardiology high specialties Luigi Sacco Hospital Via Giovanni Battista Grassi 74 Milano20157 Italy

ISBN: (纸本)9783030687625

Heart diseases are still among the main causes of death in the world population. The use of tools able to discriminate early this type of problem, even by non-specialized medical personnel on an outpatient basis, would put a decrease in health pressure on hospital centers and a better patient prognosis. This paper focuses on the problem of cardiac akinesis, a condition attributable to a very large number of pathologies, and a possible serious complication for SARS-Covid19 patients. In particular, we considered echocardiographic images of both akinetic and healthy patients. The dataset, containing echocardiograms of around 700 patients, has been supplied by Sacco hospital of Milan (Italy). We implemented a modified ResNet34 architecture and we tested the model under various combinations of parameters. The final best performing model was able to achieve a F1-score of 0.91 in the binary classification Akinetic vs. Normokinetic. © 2021, Springer Nature Switzerland AG.

关键词： Echocardiography

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