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Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

Medical Image Analysis 2022年 78卷 102396-102396页

作者： Subramaniam, Pooja Kossen, Tabea Ritter, Kerstin Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar Madai, Vince I. CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Max-von-Laue-Str. 2 Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom QUEST-Center for Transforming Biomedical Research Berlin Institute of Health Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark

关键词： Generative adversarial networks

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Designing a Pseudo-Haptics Study for Virtual Anesthesia Skills Development

Designing a Pseudo-Haptics Study for Virtual Anesthesia Skil...

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IEEE Games Media Entertainment (GEM)

作者： Bill Kapralos Alvaro Quevedo KC Collins Celina Da Silva Eva Peisachovich Kamen Kanev Michael Jenkin Adam Dubrowski Fahad Alam maxSIMhealth. Ontario Tech University Oshawa Ontario Canada Software and Informatics Research Centre. Ontario Tech University Oshawa Ontario Canada Collaborative Human Immersive Interaction Lab Toronto Canada School of Information Technology Carleton University Ottawa Ontario Canada Department of Health York University Toronto Ontario Canada Research Institute of Electronics Shizuoka University Hamamatsu Japan Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Canada Faculty of Health Sciences Ontario Tech University Oshawa Ontario Canada Department of Anesthesia Sunnybrook Health Science Centre Toronto Ontario Canada

ISBN: (纸本)9781665461399

Pseudo-haptics refers to the simulation of haptic sensations without the use of haptic interfaces, using, for example, audiovisual feedback and kinesthetic cues. Given the COVID-19 pandemic and the shift to online learning, there has been a recent interest in pseudo-haptics as it can help facilitate psychomotor skills development away from simulation centers and laboratories. Here we present work-in-progress that describes the study design of a pseudo-haptics for virtual anesthesia skills development. We anticipate this work will provide greater insight to pseudo-haptics and its application to anesthesia-based training.

关键词： Training COVID-19 Pandemics laboratories Entertainment industry Games Media

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The influence of synchrony and sensory modality on the person perception process in computer-mediated groups

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Journal of computer-Mediated Communication 2005年第3期10卷 00-00页

作者： Nowak, Kristine L. Watt, James Walther, Joseph B. Communication Science Department University of Connecticut United States Rensselaer Social and Behavioral Research Laboratory Rensselaer Polytechnic Institute United States Department of Communication Cornell University United States Human Computer Interaction Lab. University of Connecticut United States Department of Communication Sciences University of Connecticut 850 Bolton Road U-1085 Storrs CT 06269 United States Rensselaer Social and Behavioral Research Laboratory United States Department of Language Literature and Communication Rensselaer Polytechnic Institute United States James Watt LL and C Rensselaer Polytechnic Institute 110 8th Street Troy NY 12180 United States Dept. of Communication Cornell University 336 Kennedy Hall Ithaca NY 14853-4203 United States

This study examined the effects of synchrony and the number of cues on the person perception process in computer-mediated communication. One hundred and forty-two participants in groups of three or four engaged in collaboration over five weeks to develop oral reports, using alternate versions of communication systems or meeting face-to-face. Consistent with the hyperpersonal model, those using low cue media felt their partners were more credible, and reported more social attraction, less uncertainty, and more involvement in the interaction than those using high cue media. People interacting with synchronous media felt increased social attraction, self-reported involvement, and certainty. They also felt that their conversations were more effective, although this effect appeared mainly in low cue groups. Results of an exploratory path analysis suggest that future research should focus on causal chains rather than direct effects, and that intervening variables (such as involvement) may be central to our understanding of the effects of communication technology systems. © 2005 Journal of computer-Mediated Communication.

关键词： Communication systems

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QA-KGNet: A Language Model-Driven Knowledge Graph Question-Answering Model

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Ruan Jian Xue Bao/Journal of Software 2023年第10期34卷

作者： Qiao, Shao-Jie Yang, Guo-Ping Yu, Yong Han, Nan Qin, Xiao Qu, Lu-Lu Ran, Li-Qiong Li, He School of Software Engineering Chengdu University of Information Technology Chengdu610225 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China School of Management Chengdu University of Information Technology Chengdu610225 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School of Computer Science and Technology XiDian University Xi’an710071 China

The question-answering system based on knowledge graphs can analyze user questions, and has become an effective way to retrieve relevant knowledge and automatically answer the given questions. The knowledge graph-based question-answering system usually uses a neural program induction model to convert natural language question into a logical form, and the answer can be obtained by executing the logical form on the knowledge graph. However, the knowledge question-answering system by using pre-trained language models and knowledge graphs involves two challenges: 1) given the QA (Question-answering) context, relevant knowledge needs to be identified from a large KG (Knowledge Graph);2) we need to perform the joint reasoning on QA context and KG. Based on these challenges, a language model-driven knowledge graph question-answering model is proposed, which connects the QA context and KG to form a joint graph, and uses a language model to calculate the relevance of the given QA context nodes and KG nodes, and a multi-head graph attention network is employed to update the node representation. Extensive experiments on the CommonsenseQA, OpenBookQA and MedQA-USMLE real datasets are conducted to evaluate the performance of QA-KGNet and the experimental results show that QA-KGNet outperforms existing benchmark models and exhibits excellent structured reasoning capability. © 2023 Chinese Academy of Sciences. All rights reserved.

关键词： Knowledge graph

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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7.3: Compare Variation of User's EEG Owing to Parallax Cue

引用

SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Haichen Hu Danli Wang Kang Yue Xinpan Yang Steve C. Chiu Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello America

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further.

关键词： EEG Parallax RDS 3D

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Corrigendum to “Bodily sensation maps: exploring a new direction for detecting emotions from user self-reported data” [International Journal of human - computer Studies (2018) 113, 32–47]

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International Journal of human-computer Studies 2018年 120卷 125-125页

作者： Iván García-Magariño Luca Chittaro Inmaculada Plaza Department of Computer Science and Engineering of Systems Escuela Universitaria Politécnica de Teruel University of Zaragoza c/ Atarazana 2 44003 Teruel Spain Instituto de Investigación Sanitaria Aragón University of Zaragoza Zaragoza Spain Human-Computer Interaction Lab Department of Mathematics Computer Science and Physics University of Udine Via delle Scienze 206 33100 Udine Italy Department of Electronics Engineering and Communications Escuela Universitaria Politécnica de Teruel University of Zaragoza c/ Atarazana 2 44003 Teruel Spain

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New Methods on Image Analysis, Two Applications: An Augmented Reality Application for Heartbeat Sound Analysis And A MRI Brain Injury Image Analysis

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Journal of Physics: Conference Series 2019年第1期1229卷

作者： L J Lechuga T Yamamoto O López G Lechuga Japanese International Cooperation Agency (JICA) trainee program at the Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi Ishikawa / 921 - 8501 Japan Human interaction laboratory Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi Ishikawa / 921 - 8501 Japan Industrial Engineering Department ITESM calle del Puente 222 Ejidos de Huipulco 13480 CDMX Mexico Computer Vision Detection ENGIE 1 place Samuel de Champlain 92400 Courbevoie- France

Traditional statistical methods have become insufficient when applied to image analysis. The increasing size of data volume and its complexity demands new statistical approaches and algorithms. Current methods imply losing intrinsic data structures, for example when data comes from multiway arrays. In this work we concentrate in two applications i) A pre-diagnostic smartphone application for detection of cardiovascular abnormalities through the analysis of heartbeat sounds and the use of augmented reality for displaying valuable information to the end user in an immersive experience. Using the latest augmented reality smartphone applications, a digital stethoscope, heartbeat audios and classification using neural networks, we measure a user's heartbeat and output in real time, a pre-diagnostic of their current cardiovascular health. ii) A study concerning comatose patients, based on a Diffusion Tensor Image Magnetic Resonance Imaging (MRI) dataset that predicts long-term outcome for patients having suffered a brain traumatic injury. MRI images were obtained from 104 comatose patients, 65 with positive outcome and 39 with negative outcome, 39 controls were used. The fact that each volumetric image led into a 143x255726x4 tensor input, is used to briefly explain how new multiway methods could be useful in image analysis methods.

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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