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Deep-learning two-photon fiberscopy for video-rate brain imaging in freely-behaving mice (vol 13, 1534, 2022)

NATURE COMMUNICATIONS 2022年第1期13卷 1-9页

作者： Guan, Honghua Li, Dawei Park, Hyeon-Cheol Li, Ang Yue, Yuanlei Gau, Yung-Tian A. Li, Ming-Jun Bergles, Dwight E. Lu, Hui Li, Xingde Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD 21218 USA Department of Biomedical Engineering Johns Hopkins University School of Medicine Baltimore MD 21205 USA Johns Hopkins Kavli Neuroscience Discovery Institute Baltimore MD 21218 USA Department of Pharmacology and Physiology School of Medicine and Health Sciences George Washington University Washington DC 20052 USA Solomon H. Snyder Department of Neuroscience Johns Hopkins University School of Medicine Baltimore MD 21205 USA Science and Technology Division Corning Incorporated Corning NY 14831 USA

Scanning two-photon (2P) fiberscopes (also termed endomicroscopes) have the potential to transform our understanding of how discrete neural activity patterns result in distinct behaviors, as they are capable of high resolution, sub cellular imaging yet small and light enough to allow free movement of mice. However, their acquisition speed is currently suboptimal, due to opto-mechanical size and weight constraints. Here we demonstrate significant advances in 2P fiberscopy that allow high resolution imaging at high speeds (26 fps) in freely-behaving mice. A high-speed scanner and a down-sampling scheme are developed to boost imaging speed, and a deep learning (DL) algorithm is introduced to recover image quality. For the DL algorithm, a two-stage learning transfer strategy is established to generate proper training datasets for enhancing the quality of in vivo images. Implementation enables video-rate imaging at ~26 fps, representing 10-fold improvement in imaging speed over the previous 2P fiberscopy technology while maintaining a high signal-to-noise ratio and imaging resolution. This DL-assisted 2P fiberscope is capable of imaging the arousal-induced activity changes in populations of layer2/3 pyramidal neurons in the primary motor cortex of freely-behaving mice, providing opportunities to define the neural basis of behavior.

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

arXiv

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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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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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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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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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Personnel System Development with Integrated Military Education Information System (MEIS) in Supporting the Personnel Position and Career 7

Personnel System Development with Integrated Military Educat...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Setyaningrum, Leli Supangkat, Suhono H. Warnars, Harco Leslie Hendric Spits Abraham, Juneman Abdurachman, Edi Bina Nusantara University Doctorate Program of Computer Science Jakata Barat11530 Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung40132 Indonesia Nusantara University Graduate Program Doctor of Computer Science Bina Computer Science Department BINUS Jakarta11530 Indonesia Bina Nusantara University BINUS Faculty of Humanities Psychology Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Personnel and education data very important for organizations, including the military. All data related to military personnel will give effect positions and careers in the military field. The integration of personnel and education data can assist in processing and decision making. An analysis is needed so that the developed system can be useful for the long term. Analysis with IT balanced scorecard and COBIT® 2019, will be able to help and fill the gaps in the existing system based on the objectives of the military organization itself. It is shown that the domains BAI05 (Managed Organizational Change) and DSS02 (Managed Service Requests and Incidents) are still not being implemented well. The system developed will have the features and capabilities to support the organization in achieving its goals, including in the field of staffing and education. Besides, it provides convenience in making decisions in terms of positions related to personnel careers. © 2020 IEEE.

关键词： Decision making

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An Interactive Control Approach to 3D Shape Reconstruction

An Interactive Control Approach to 3D Shape Reconstruction

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American Control Conference

作者： Bipul Islam Ji Liu Anthony Yezzi Romeil Sandhu Department of Computer Science & Biomedical Informatics Stony Brook University Stony Brook NY Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY Department of Electrical and Computer Engineering Georgia Tech Atlanta GA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

The ability to accurately reconstruct the 3D facets of a scene is one of the key problems in robotic vision. However, even with recent advances with machine learning, there is no high-fidelity universal 3D reconstruction method for this optimization problem as schemes often cater to specific image modalities and are often biased by scene abnormalities. Simply put, there always remains an "information" gap due to the dynamic nature of real-world scenarios. To this end, we demonstrate a feedback control framework which invokes operator inputs (also prone to errors) in order to augment existing reconstruction schemes. For proof-of-concept, we choose a classical region-based stereoscopic reconstruction approach and show how an ill-posed model can be augmented with operator input to be much more robust to scene artifacts. We provide necessary conditions for stability via Lyapunov analysis and perhaps more importantly, we show that the stability depends on a notion of absolute curvature. Mathematically, this aligns with previous work that has shown Ricci curvature as proxy for functional robustness of dynamical networked systems. We conclude with results that show how our method can improve standalone reconstruction schemes.

关键词： Three-dimensional displays Image reconstruction Two dimensional displays Surface reconstruction Image segmentation Shape Cameras

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Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation

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JMIR Bioinformatics and Biotechnology 2022年第1期3卷 e36660页

作者： Li, Zhongqiang Li, Zheng Yao, Luke Chen, Qing Zhang, Jian Li, Xin Feng, Ji-Ming Li, Yanping Xu, Jian Division of Electrical and Computer Engineering College of Engineering Louisiana State University Baton Rouge LA United States Department of Visualization Texas A & M University College Station TX United States Department of Comparative Biomedical Science School of Veterinary Medicine Louisiana State University Baton Rouge LA United States School of Environment and Sustainability University of Saskatchewan Saskatoon SK Canada

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results: In both the whole-image and left-and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung r

关键词： chest X-ray radiography COVID-19 multiple-inputs convolutional neural network screening critical COVID regions

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AlexNet-based deep convolutional neural network optimized with group teaching optimization algorithm (GTOA) for paediatric bone age assessment from hand X-ray images

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The Journal of Photographic Science 2024年第3期72卷

作者： E. P. Hemand Mohandass G. Francis H. Shajin D. Kirubakaran a Department of Computer science and Engineering NIT Calicut Kozhikode India b Department of Biomedical Engineering Agni College of Technology Chennai India c Research and Development Xpertmindz Innovative Solutions Private Limited Kuzhithurai Tamil Nadu India d Department of Electrical and Electronics Engineering St. Joseph's Institute of Technology Chennai India

ABSTRACTBone age assessment is used to diagnose paediatric growth because some types of bone diseases occur in childhood. To overcome these issues, AlexNet-Based Deep Convolutional Neural Network Optimized with the Group Teaching Optimization Algorithm is proposed. First, input images are gathered via RSNA paediatric bone age dataset. These images are preprocessed using Wavelet Packet Transform Cochlear Filter Bank. Then input hand X-ray images’ ROI is segmented using Bayesian fuzzy clustering. Then segmented ROI region is fed to ADCNN that accurately predicts BAA. In general, ADCNN does not divulge any optimization techniques adopted for determining the optimal parameters and ensuring accurate classification. Therefore, the GTOA is used to optimize the ADCNN weight parameters. The proposed approach is done in MATLAB and various performance metrics such as accuracy, F-score, sensitivity, precision, specificity, CCC and CC. The BAA-ADCNN-GTOA method provides higher accuracy 23.75%, 17.97%, 31.65% compared with existing methods, like BAA-CNN-RRNN, BAA-RNN-AF-SFO, BAA-U-Net-CTO- WOA, respectively.

关键词： AlexNet-based deep convolutional neural network (ADCNN) Bayesian fuzzy clustering bone age assessment group teaching optimization algorithm (GTOA) Wavelet Packet Transform Cochlear Filter Bank region of Interest RSNA paediatric bone age dataset X-ray images

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