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Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI

Medical image analysis 2025年 104卷 103630页

作者： Nicolás Gaggion Benjamin A Matheson Yan Xia Rodrigo Bonazzola Nishant Ravikumar Zeike A Taylor Diego H Milone Alejandro F Frangi Enzo Ferrante Institute for Signals Systems and Computational Intelligence sinc(i) CONICET-UNL Santa Fe Argentina Institute of Computer Sciences (ICC) CONICET-Universidad de Buenos Aires Ciudad Autónoma de Buenos Aires Argentina. Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) School of Computing University of Leeds Leeds UK. Institute for Signals Systems and Computational Intelligence sinc(i) CONICET-UNL Santa Fe Argentina. Christabel Pankhurst Institute The University of Manchester Manchester UK Centre for Computational Imaging and Modelling in Medicine (CIMIM) Department of Computer Science School of Engineering and Division of Informatics Imaging and Data Science School of Health Sciences The University of Manchester Manchester UK NIHR Manchester Biomedical Research Centre Manchester Academic Health Sciences Centre University of Manchester Manchester UK Medical Imaging Research Centre (MIRC) Department of Cardiovascular Sciences KU Leuven Leuven Belgium. Institute of Computer Sciences (ICC) CONICET-Universidad de Buenos Aires Ciudad Autónoma de Buenos Aires Argentina. Electronic address: eferrante@sinc.unl.edu.ar.

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. Traditional approaches typically follow complex multi-step pipelines, first segmenting images and then reconstructing meshes, making them time-consuming and prone to error propagation. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multi-view HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity meshes from CMR images. Multi-view HybridVNet outperforms the state-of-the-art, achieving improvements of up to ∼27% reduction in Mean Contour Distance (from 1.86 mm to 1.35 mm for the LV Myocardium), up to ∼18% improvement in Hausdorff distance (from 4.74 mm to 3.89 mm, for the LV Endocardium), and up to ∼8% in Dice Coefficient (from 0.78 to 0.84, for the LV Myocardium), highlighting its superior accuracy.

关键词： Cardiac imaging Geometric deep learning Hybrid graph convolutional neural network Volume-to-mesh

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A sequential tree-based classifier for personalized biomarker testing of Alzheimer's disease risk

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IISE Transactions on Healthcare systems engineering 2017年第4期7卷 248-260页

作者： Si, Bing Yakushev, Igor Li, Jing Industrial Engineering School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ United States Nuclear Medicine Department Technische Universität München Germany

Using baseline biomarkers to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) has considerable clinical interest in recent years. The existing studies have several limitations, including unsatisfactory accuracy due to MCI heterogeneity, use of conventional classification models that require biomarkers to be measured all at once instead of sequentially and as needed, and use of raw numerical measurement of the biomarkers instead of discretized levels that are more robust to measurement errors and provide convenience for clinical utilization. To tackle these limitations, we propose a novel sequence tree-based classifier (STC) for predicting the conversion of MCI to AD. Different from conventional classification models, STC achieves a sequential, as-needed use of biomarkers and a three-category classification (high-risk converter, low-risk converter, and inconclusive diagnosis) by finding an optimal sequence of biomarkers and two-sided cutoffs of each biomarker that satisfy pre-specified accuracy requirements while minimizing the proportion of inconclusive diagnosis. STC is also a personalized approach, as it allows patient characteristic variables to be included to help identify patient-specific cutoffs for each biomarker. We apply STC to two important clinical applications using the data from the worldwide Alzheimer's Disease Neuroimaging Initiative project: prediction of MCI conversion and patient selection for AD-related clinical trials. © 2017 “IISE”.

关键词： Alzheimer's disease biomarker Classification personalized medicine sequential decision making

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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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Integration of multiple health information systems for quality improvement of radiologic care

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IISE Transactions on Healthcare systems engineering 2017年第3期7卷 169-180页

作者： Wang, Kun Zwart, Christine Wellnitz, Clinton Wu, Teresa Li, Jing Industrial Engineering School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ United States Department of Radiology May Clinic in Arizona Scottsdale AZ United States

In radiology, several Health Information systems (HISs) are commonly used: Electronic Health Records (EHR), Radiology Information System (RIS), and Picture Archive and Communication System (PACS). Each HIS records partial and complementary information about the radiologic care process. Depending on the institution, the HISs that touch radiology can be distinct, disparate, and with different formats and meta information. We note no reported research on integrating multiple HISs to allow for an end-to-end tracking of the care patients receive. Therefore, the current Quality of Care (QoC) research is limited as it can only utilize data for partial workflow analysis. A comprehensive assessment of the QoC in radiology requires HISs be integrated such that key metrics can be extracted. We developed a novel technology called Department Data Depot (DDD) that integrates multiple HISs in radiology. We propose nine metrics defined upon DDD data measuring various dimensions of care quality. To demonstrate the clinical utility of DDD, we developed and deployed the Radiology Quality Dashboard (RQD) at Mayo Clinic in Arizona. Four use cases illustrate how RQD is used to assist the clinical practice. A case study on how DDD enabled an effective intervention for reducing lengthy radiologist turnaround times (TATs) is presented. © 2017 “IISE”.

关键词： health care Health information system quality improvement radiology

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Unfolding the Mutual Relation between Timeliness and Scalability in Cloud Monitoring

Unfolding the Mutual Relation between Timeliness and Scalabi...

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2018 IEEE Symposium on Computers and Communications, ISCC 2018

作者： Da Cunha Rodrigues, Guilherme Calheiros, Rodrigo N. Dos Santos, Glederson Lessa Guimaraes, Vinicius Tavares Granville, Lisandro Zambenedetti Tarouco, Liane Buyya, Rajkumar Science and Technology sul Rio-Grandense Federal Institute of Education Charqueadas Brazil School of Computing Engineering and Mathematics Western Sydney University Sydney Australia Computer Networks Group Institute of Informatics Federal University of Rio Grande Do sul Porto Alegre Brazil Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9781538669501

Cloud computing is a suitable solution for professionals, companies, and institutions that need to have access to computational resources on demand. Clouds rely on proper management to provide such computational resources with adequate quality of service, which is established by Service Level Agreements (SLAs), to customers. In this context, cloud monitoring is a critical function to achieve such proper management. Cloud monitoring systems have to accomplish requirements to perform its functions properly, and currently, there are plenty of requirements which includes: timeliness, adaptability, comprehensiveness, and scalability. However, such requirements usually have mutual influence, which is positive or negative, among themselves, and it has prevented the development of complete cloud monitoring solutions. This paper presents a mathematical model to predict the mutual influence between timeliness and scalability, which is a step forward in cloud monitoring because it paves the way for the development of complete monitoring solutions. It complements our previous work that identified the monitoring parameters (e.g., frequency sampling, amount of monitoring data) that influence timeliness and scalability. Evaluations present the effectiveness of the mathematical model based on a comparison of the results provided by the mathematical model and the results obtained via simulation. © 2018 IEEE.

关键词： Scalability

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Recency predicts bursts in the evolution of author citations

arXiv

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arXiv 2019年

作者： Silva, Filipi Nascimento Tandon, Aditya Amancio, Diego Raphael Flammini, Alessandro Menczer, Filippo Milojević, Staša Fortunato, Santo Indiana University Network Science Institute United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil

The citations process for scientific papers has been studied extensively. But while the citations accrued by authors are the sum of the citations of their papers, translating the dynamics of citation accumulation from the paper to the author level is not trivial. Here we conduct a systematic study of the evolution of author citations, and in particular their bursty dynamics. We find empirical evidence of a correlation between the number of citations most recently accrued by an author and the number of citations they receive in the future. Using a simple model where the probability for an author to receive new citations depends only on the number of citations collected in the previous 12–24 months, we are able to reproduce both the citation and burst size distributions of authors across multiple decades. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamics

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An Advanced Quantum-Resistant Signature Scheme for Cloud Based on Eisenstein Ring

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Computers, Materials & Continua 2018年第7期56卷 19-34页

作者： Faguo Wu Xiao Zhang Wang Yao Zhiming Zheng Lipeng Xiang Wanpeng Li Key Laboratory of Mathematics Informatics and Behavioral SemanticsMinistry of EducationSchool of Mathematics and Systems ScienceBeihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100191China The Fifth Electronics Research Institute of Ministry of Industry and Information Technology Guangzhou510610China Department of Electrical and Electronic Engineering City University of LondonUK

Signature,widely used in cloud environment,describes the work as readily identifying its *** existing signature schemes in the literature mostly rely on the Hardness assumption which can be easily solved by quantum *** this paper,we proposed an advanced quantum-resistant signature scheme for Cloud based on Eisenstein Ring(ETRUS)which ensures our signature scheme proceed in a lattice with higher *** proved that ETRUS highly improve the performance of traditional lattice signature ***,the Norm of polynomials decreases significantly in ETRUS which can effectively reduce the amount of polynomials convolution ***,storage complexity of ETRUS is smaller than classical ***,according to all convolution of ETRUS enjoy lower degree polynomials,our scheme appropriately accelerate 56.37%speed without reducing its security level.

关键词： Signature quantum-resistant Eisenstein Ring ETRUS

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Ranking with adaptive neighbors

arXiv

引用

arXiv 2018年

作者： Li, Muge Li, Liangyue Nie, Feiping Cixi Hanvos Yucai High School Ningbo315300 China School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ85281 United States Northwestern Polytechnical University Xian710072 China

Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, and document retrievals. Stateof-the-Art approaches have mainly focused on capturing the underlying geometry of the data manifolds. Graphbased approaches, in particular, define various diffusion processes on weighted data graphs. Despite success, these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix. In this study, we propose a new ranking algorithm that simultaneously learns the data affinity matrix and the ranking scores. The proposed optimization formulation assigns adaptive neighbors to each point in the data based on the local connectivity, and the smoothness constraint assigns similar ranking scores to similar data points. We develop a novel and efficient algorithm to solve the optimization problem. Evaluations using synthetic and real datasets suggest that the proposed algorithm can outperform the existing methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Query processing

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Comparing scientific and technological impact of biomedical research

arXiv

引用

arXiv 2018年

作者： Ke, Qing Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Traditionally, the number of citations that a scholarly paper receives from other papers is used as the proxy of its scientific impact. Yet citations can come from domains outside the scientific community, and one such example is through patented technologies—paper can be cited by patents, achieving technological impact. While the scientific impact of papers has been extensively studied, the technological aspect remains less known in the literature. Here we aim to fill this gap by presenting a comparative study on how 919 thousand biomedical papers are cited by U.S. patents and by other papers over time. We observe a positive correlation between citations from patents and from papers, but there is little overlap between the two domains in either the most cited papers, or papers with the most delayed recognition. We also find that the two types of citations exhibit distinct temporal variations, with patent citations lagging behind paper citations for a median of 6 years for the majority of papers. Our work contributes to the understanding of the technological impact of papers. Copyright © 2018, The Authors. All rights reserved.

关键词： Patents and inventions

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Benchmarking in Optimization: Best Practice and Open Issues

arXiv

引用

arXiv 2020年

作者： Bartz-Beielstein, Thomas Doerr, Carola Bossek, Jakob Chandrasekaran, Sowmya Eftimov, Tome Fischbach, Andreas Kerschke, Pascal López-Ibáñez, Manuel Malan, Katherine M. Moore, Jason H. Naujoks, Boris Orzechowski, Patryk Volz, Vanessa Wagner, Markus Weise, Thomas Institute for Data Science Engineering and Analytics TH Köln Germany Sorbonne Université CNRS LIP6 Paris France Optimisation and Logistics School of Computer Science University of Adelaide Adelaide Australia Computer Systems Department Jožef Stefan Institute Ljubljana Slovenia Statistics and Optimization Group University of Münster Münster Germany School of Computer Science and Engineering University of Málaga Málaga Spain Department of Decision Sciences University of South Africa Pretoria South Africa Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Automatics AGH University of Science and Technology Krakow Poland modl.ai Copenhagen Denmark Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei China

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic *** Codes 68W50. Copyright © 2020, The Authors. All rights reserved.

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