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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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Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort (vol 54, pg 134, 2022)

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NATURE GENETICS 2024年第3期56卷 554-554页

作者： Qin, Youwen Havulinna, Aki S. Liu, Yang Jousilahti, Pekka Ritchie, Scott C. Tokolyi, Alex Sanders, Jon G. Valsta, Liisa Brozynska, Marta Zhu, Qiyun Tripathi, Anupriya Vazquez-Baeza, Yoshiki Loomba, Rohit Cheng, Susan Jain, Mohit Niiranen, Teemu Lahti, Leo Knight, Rob Salomaa, Veikko Inouye, Michael Meric, Guillaume Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia School of BioSciences The University of Melbourne Melbourne Victoria Australia Department of Clinical Pathology Melbourne Medical School The University of Melbourne Melbourne Victoria Australia Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus & University of Cambridge Cambridge UK The Alan Turing Institute London UK Department of Infectious Diseases Central Clinical School Monash University Melbourne Victoria Australia Department of Public Health and Welfare Finnish Institute for Health and Welfare Helsinki Finland Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland Department of Medicine Turku University Hospital and University of Turku Turku Finland Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Ecology and Evolutionary Biology Cornell University Ithaca NY USA Cornell Institute for Host-Microbe Interaction and Disease Cornell University Ithaca NY USA Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA Division of Biological Sciences University of California San Diego La Jolla CA USA Center for Microbiome Innovation University of California San Diego La Jolla CA USA Department of Computer Science & Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA NAFLD Research Center Department of Medicine University of California San Diego La Jolla CA USA Smidt Heart Institute Cedars-Sinai Medical Center Los Angeles CA USA Department of Computing Univer

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Illinois Express Quantum Network (IEQNET): Metropolitan-scale experimental quantum networking over deployed optical fiber

arXiv

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arXiv 2021年

作者： Chung, Joaquin Kanter, Gregory Lauk, Nikolai Valivarthi, Raju Wu, Wenji Ceballos, Russell R. Peña, Cristián Sinclair, Neil Thomas, Jordan Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt LLC 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States Department of Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Dr. ChicagoIL60628 United States Pittsburgh Quantum Institute B4 Thaw Hall 4061 O'Hara Street PittsburghPA15260 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-nodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. © 2021, CC BY.

关键词： Optical fibers

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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A quantum annealing approach to ionic diffusion in solids

arXiv

引用

arXiv 2019年

作者： Utimula, Keishu Ichibha, Tom Prayogo, Genki I. Hongo, Kenta Nakano, Kousuke Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

We have developed a framework for using quantum annealing computation to evaluate a key quantity in ionic diffusion in solids, the correlation factor. Existing methods can only calculate the correlation factor analytically in the case of physically unrealistic models, making it difficult to relate microstructural information about diffusion path networks obtainable by current ab initio techniques to macroscopic quantities such as diffusion coefficients. We have mapped the problem into a quantum spin system described by the Ising Hamiltonian. By applying our framework in combination with ab initio technique, it is possible to understand how diffusion coefficients are controlled by temperatures, pressures, atomic substitutions, and other factors. We have calculated the correlation factor in a simple case with a known exact result by a variety of computational methods, including simulated quantum annealing on the spin models, the classical random walk, the matrix description, and quantum annealing on D-Wave with hybrid solver. This comparison shows that all the evaluations give consistent results with each other, but that many of the conventional approaches require infeasible computational costs. Quantum annealing is also currently infeasible because of the cost and scarcity of Q-bits, but we argue that when technological advances alter this situation, quantum annealing will easily outperform all existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Hamiltonians

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Stochastic estimations of a total number of classes for the clusterings with too enormous samples to be accommodate into a clustering engine

arXiv

引用

arXiv 2019年

作者： Utimula, Keishu Prayogo, Genki I. Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total number of the possibility. Concrete situations are explained taking examples of atomic substitutions in the supercell modeling of alloys. The number of the possibility sometimes amounts to ∼ trillion being too large to be accommodate. It is hence not practically feasible to identify how many irreducible classes exist by straightforward manner even though there are several tools available to perform the clustering. We have developed a stochastic framework to avoid the shortage of capacity, providing a method to estimate the total number of irreducible classes (the order of the classes) as a statistical estimate. A prominent conclusion derived here is that the statistical variation of the number of classes at each sampling trial is working as a promising measure to estimate the order. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Author Correction: Inferring structural variant cancer cell fraction

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Nature communications 2022年第1期13卷 7568页

作者： Marek Cmero Ke Yuan Cheng Soon Ong Jan Schröder Niall M Corcoran Tony Papenfuss Christopher M Hovens Florian Markowetz Geoff Macintyre Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. cmerom@unimelb.edu.au. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. cmerom@unimelb.edu.au. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. cmerom@unimelb.edu.au. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. cmerom@unimelb.edu.au. Murdoch Children's Research Institute Parkville VIC 3052 Australia. cmerom@unimelb.edu.au. School of Computing Science University of Glasgow Sir Alwyn Williams Building Glasgow G12 8RZ UK. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. Electrical and Electronic Engineering University of Melbourne Parkville VIC 3010 Australia. Machine Learning Research Group Data61 Canberra ACT 2601 Australia. Research School of Computer Science Australian National University Canberra ACT 2601 Australia. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. geoff.macintyre@cruk.cam.ac.uk. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. geoff.macintyre@cruk.cam.ac.uk.

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THE ARCTIC

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Bulletin of the American Meteorological Society 2024年第8期105卷 S277-S330页

作者： Druckenmiller, M.L. Thoman, R.L. Moon, T.A. National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder Colorado International Arctic Research Center University of Alaska Fairbanks Fairbanks Alaska National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Colorado Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona Biospherical Instruments Inc. San Diego California Geophysical Institute University of Alaska Fairbanks Fairbanks Alaska Plant Soils and Climate Department Utah State University Logan Utah Norwegian Institute for Nature Research Trondheim Norway FRAM – High North Research Centre for Climate and the Environment Tromsø Norway Geological Survey of Denmark and Greenland (GEUS) Copenhagen Denmark NOAA/NWS Alaska Region Anchorage Alaska Alaska Native Tribal Health Consortium Anchorage Alaska Geological Survey of Canada Natural Resources Canada Ottawa Canada NOAA Chemical Sciences Laboratory Boulder Colorado Arctic Geophysics Department University Centre in Svalbard Longyearbyen Norway Geology Department University Centre in Svalbard Longyearben Norway Centre National de Recherches Météorologiques Météo-France/CNRS Toulouse France Climate Research Division Environment and Climate Change Canada Toronto Canada Norwegian Polar Institute Fram Centre Tromsø Norway Danish Meteorological Institute Copenhagen Denmark Department of Physics University of Toronto Toronto Canada Department of Environmental Sciences University of Virginia Charlottesville Virginia Department of Geographical Sciences University of Maryland College Park Maryland University of Liège Liège Belgium Environment and Climate Change Canada Toronto Canada Northern Rocky Mountain Science Center U.S. Geological Survey Bozeman Montana Arctic C

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Ti interstitial flows giving rutile TiO2 reoxidation process enhanced in (001) surface

arXiv

引用

arXiv 2019年

作者： Ichibha, Tom Benali, Anouar Hongo, Kenta Maezono, Ryo School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Computational Science Division Argonne National Laboratory 9700 Cass Avenue LemontIL60439 United States Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

We revisited ab initio evaluations of the energy barriers along the possible diffusion paths of the defects in rutile TiO2. By using a method carefully considering the cancellation of the self-interaction, Ti interstitials hopping along c-axis are identified as the major diffusion directing to [001] surface. The conclusion is contradicting to any of previous theoretical works, and the discrepancy is explained by the overestimation of the radius of defects due to the poor cancellations in the previous works. The updated prediction here can explain the superior photocatalysis activity in [001] surface to [110]. Copyright © 2019, The Authors. All rights reserved.

关键词： Titanium dioxide

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Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging

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European heart journal. Imaging methods and practice 2024年第1期2卷 qyae042页

作者： Xiang Chen Yan Xia Erica Dall'Armellina Nishant Ravikumar Alejandro F Frangi School of Computing University of Leeds Woodhouse LS2 9JT Leeds UK. School of Medicine University of Leeds Woodhouse LS2 9JT Leeds UK. Christabel Pankhurst Institute The University of Manchester Oxford Rd M13 9PL Manchester UK. Department of Computer Science School of Engineering The University of Manchester Oxford Rd M13 9PL Manchester UK. Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester Oxford Rd M13 9PL Manchester UK. NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Oxford Rd M13 9PL Manchester UK. Medical Imaging Research Center (MIRC) University Hospital Gasthuisberg UZ Herestraat 49 - bus 7003 3000 Leuven Belgium. Department of Cardiovascular Sciences KU Leuven UZ Herestraat 49 - box 911 3000 Leuven Belgium. Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 postbus 2440 3001 Leuven Belgium. Alan Turing Institute British Library 96 Euston Rd. NW1 2DB London UK.

Aims:Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart. To obtain efficient and explainable biomarkers for CVD prediction and diagnosis, it is needed to jointly consider both representations. Methods and results:We design a novel multi-channel variational auto-encoder, mesh-image variational auto-encoder, to learn joint representation of paired mesh and image. After training, the shape-aware image representation (SAIR) can be learned directly from the raw images and applied for further CVD prediction and diagnosis. We demonstrate our method on data from UK Biobank study and two other datasets via extensive experiments. In acute myocardial infarction prediction, SAIR achieves 81.43% accuracy, significantly higher than traditional biomarkers like metadata and clinical indices (left ventricle and right ventricle clinical indices of cardiac function like chamber volume, mass, and ejection fraction). Conclusion:Our mesh-image variational auto-encoder provides a novel approach for 3D cardiac mesh reconstruction from images. The extraction of SAIR is fast and without need of segmentation masks, and its focussing can be visualized in the corresponding cardiac meshes. SAIR archives better performance than traditional biomarkers and can be applied as an efficient supplement to them, which is of significant potential in CVD analysis.

关键词： CVD diagnosis CVD prediction cardiac biomarker mesh reconstruction multi-channel VAE

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