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arXiv 2022年

作者： Angelopoulos, V. Zhang, X.-J. Artemyev, A.V. Mourenas, D. Tsai, E. Wilkins, C. Runov, A. Liu, J. Turner, D.L. Li, W. Khurana, K. Wirz, R.E. Sergeev, V.A. Meng, X. Wu, J. Hartinger, M.D. Raita, T. Shen, Y. An, X. Shi, X. Bashir, M.F. Shen, X. Gan, L. Qin, M. Capannolo, L. Ma, Q. Russell, C.L. Masongsong, E.V. Caron, R. He, I. Iglesias, L. Jha, S. King, J. Kumar, S. Le, K. Mao, J. McDermott, A. Nguyen, K. Norris, A. Palla, A. Roosnovo Tam, J. Xie, E. Yap, R.C. Ye, S. Young, C. Adair, L.A. Shaffer, C. Chung, M. Cruce, P. Lawson, M. Leneman, D. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B.W. Eldin, S. Fitzgibbon, L. Flemming, A. Frederick, D.M. Gilbert, A. Hesford, B. Krieger, R. Lian, K. McKinney, E. Miller, J.P. Pedersen, C. Qu, Z. Rozario, R. Rubly, M. Seaton, R. Subramanian, A. Sundin, S.R. Tan, A. Thomlinson, D. Turner, W. Wing, G. Wong, C. Zarifian, A. Earth Planetary and Space Sciences Department Institute of Geophysics and Planetary Physics University of California Los Angeles Los AngelesCA90095 United States University of Texas at Dallas RichardsonTX75080 United States CEA DAM DIF Arpajon France Atmospheric and Oceanic Sciences Departments University of California Los AngelesCA United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Astronomy Center for Space Physics Boston University BostonMA United States Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States School of Mechanical Industrial Manufacturing Engineering Oregon State University CorvallisOR97331 United States University of St. Petersburg St. Petersburg Russia Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Space Science Institute BoulderCO80301 United States Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Materials Science and Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Deloitte Consulting New YorkNY10112 United States Computer Science Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Microsoft RedmondWA98052 United States Physics and Astronomy Department University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Raybeam Inc. Mountain ViewCA94041 United States SpaceX HawthorneCA90250 United States Reliable Robotics Corporation Mountain ViewCA94043 United States Los Alamos National Laboratory Los AlamosNM87545 United States Mathematics Department University of California Los AngelesCA90095 United States Planet Labs PBC San FranciscoCA94107 United States KSAT Inc. De

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data from the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17s, or ∆L ∼ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using 2 years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 − 7 at dusk, while a smaller subset exists at L ∼ 8 − 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive

关键词： Radiation belts

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Pull message passing for nonparametric belief propagation

arXiv

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arXiv 2018年

作者： Desingh, Karthik Opipari, Anthony Jenkins, Odest Chadwicke Laboratory for Perception RObotics Grounded REasoning SystemS Department of Electrical Engineering and Computer Science Robotics Institute University of Michigan Ann ArborMI48105 United States

We present a "pull" approach to approximate products of Gaussian mixtures within message updates for Nonparametric Belief Propagation (NBP) inference. Existing NBP methods often represent messages between continuous-valued latent variables as Gaussian mixture models. To avoid computational intractability in loopy graphs, NBP necessitates an approximation of the product of such mixtures. Sampling-based product approximations have shown effectiveness for NBP inference. However, such approximations used within the traditional "push" message update procedures quickly become computationally prohibitive for multi-modal distributions over high-dimensional variables. In contrast, we propose a "pull" method, as the Pull Message Passing for Nonparametric Belief propagation (PMPNBP) algorithm, and demonstrate its viability for efficient inference. We report results using an experiment from an existing NBP method, PAMPAS, for inferring the pose of an articulated structure in clutter. Results from this illustrative problem found PMPNBP has a greater ability to efficiently scale the number of components in its mixtures and, consequently, improve inference accuracy. Copyright © 2018, The Authors. All rights reserved.

关键词： Message passing

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An In-Depth Look at TOI-3884b: a Super-Neptune Transiting a M4 Dwarf with Persistent Star Spot Crossings

arXiv

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arXiv 2023年

作者： Libby-Roberts, Jessica E. Schutte, Maria Hebb, Leslie Kanodia, Shubham Cañas, Caleb I. Stefánsson, Guðmundur Lin, Andrea S.J. Mahadevan, Suvrath Parts, Winter Powers, Luke Wisniewski, John Bender, Chad F. Cochran, William D. Diddams, Scott A. Everett, Mark E. Gupta, Arvind F. Halverson, Samuel Kobulnicky, Henry A. Kowalski, Adam F. Larsen, Alexander Monson, Andrew Ninan, Joe P. Parker, Brock A. Ramsey, Lawrence W. Robertson, Paul Schwab, Christian Swaby, Tera N. Terrien, Ryan C. Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Homer L. Dodge Department of Physics and Astronomy University of Oklahoma 440 W. Brooks Street NormanOK73019 United States Physics Department Hobart and William Smith Colleges 300 Pulteney Street GenevaNY14456 United States Department of Astronomy Cornell University 245 East Ave IthacaNY14850 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Department of Physics & Astronomy George Mason University 4400 University Drive MS 3F3 FairfaxVA22030 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States McDonald Observatory Department of Astronomy The University of Texas Austin United States Center for Planetary Systems Habitability The University of Texas Austin United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Time and Frequency Division National Institute of Standards and Technology 325 Broadway BoulderCO80305 United States NSF’s National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 Uni

We perform an in-depth analysis of the recently validated TOI-3884 system, an M4 dwarf star with a transiting super-Neptune. Using high precision light curves obtained with the 3.5 m Apache Point Observatory and radial velocity observations with the Habitable-zone Planet Finder (HPF), we derive a planetary mass of 32.6+7.3−7.4M⨁ and radius of 6.4 ± 0.2 R⨁. We detect a distinct star spot crossing event occurring just after ingress and spanning half the transit for every transit. We determine this spot feature to be wavelength-dependent with the amplitude and duration evolving slightly over time. Best-fit star spot models show that TOI-3884b possesses a misaligned (λ = 75 ± 10◦) orbit which crosses a giant pole-spot. This system presents a rare opportunity for studies into the nature of both a misaligned super-Neptune and spot evolution on an active mid-M dwarf. © 2023, CC BY.

关键词： Orbits

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TOI-3757 b: A low density gas giant orbiting a solar-metallicity M dwarf

arXiv

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arXiv 2022年

作者： Kanodia, Shubham Libby-Roberts, Jessica Cañas, Caleb I. Ninan, Joe P. Mahadevan, Suvrath Stefansson, Gudmundur Lin, Andrea S.J. Jones, Sinclaire Monson, Andrew Parker, Brock A. Kobulnicky, Henry A. Swaby, Tera N. Powers, Luke Beard, Corey Bender, Chad F. Blake, Cullen H. Cochran, William D. Dong, Jiayin Diddams, Scott A. Fredrick, Connor Gupta, Arvind F. Halverson, Samuel Hearty, Fred Logsdon, Sarah E. Metcalf, Andrew J. McElwain, Michael W. Morley, Caroline Rajagopal, Jayadev Ramsey, Lawrence W. Robertson, Paul Roy, Arpita Schwab, Christian Terrien, Ryan C. Wisniewski, John Wright, Jason T. Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States Department of Physics and Astronomy The University of California Irvine IrvineCA92697 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Department of Physics and Astronomy University of Pennsylvania 209 S 33rd St PhiladelphiaPA19104 United States McDonald Observatory Department of Astronomy The University of Texas Austin United States Center for Planetary Systems Habitability The University of Texas Austin United States Time and Frequency Division National Institute of Standards and Technology 325 Broadway BoulderCO80305 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States NSF's National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Space Vehicles Directorate Air Force Research Laboratory 3550 Aberdeen Ave. SE Kirtland AFB NM87117 United States Time and Frequency Division National Institute of Technology 325 Broadway BoulderCO80305 United States Department of Physics University of Colorado Boulder 390 UCB BoulderCO80309 United States Exoplanets and Stellar Astrophysics Laboratory NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Astronomy The University of Texas Austin Uni

We present the discovery of a new Jovian-sized planet, TOI-3757 b, the lowest density transiting planet known to orbit an M dwarf (M0V). This planet was discovered around a solar-metallicity M dwarf, using TESS photometry and confirmed with precise radial velocities from HPF and NEID. With a planetary radius of 12.0−+0.40.5 R⊕ and mass of 85.3+8.8−8.7 M⊕, not only does this object add to the small sample of gas giants (∼ 10) around M dwarfs, but also, its low density (ρ = 0.27+0.05−0.04 g cm−3) provides an opportunity to test theories of planet formation. We present two hypotheses to explain its low density;first, we posit that the low metallicity of its stellar host (∼ 0.3 dex lower than the median metallicity of M dwarfs hosting gas giants) could have played a role in the delayed formation of a solid core massive enough to initiate runaway accretion. Second, using the eccentricity estimate of 0.14 ± 0.06 we determine it is also plausible for tidal heating to at least partially be responsible for inflating the radius of TOI-3757b b. The low density and large scale height of TOI-3757 b makes it an excellent target for transmission spectroscopy studies of atmospheric escape and composition (TSM ∼ 190). We use HPF to perform transmission spectroscopy of TOI-3757 b using the helium 10830 Å line. Doing this, we place an upper limit of 6.9 % (with 90% confidence) on the maximum depth of the absorption from the metastable transition of He at ∼ 10830 Å, which can help constraint the atmospheric mass loss rate in this energy limited regime. © 2022, CC BY.

关键词： Orbits

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Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Michael Pritchard Abraham Itzhak Weinberg John A R Williams Felipe Campelo Harry Goldingay Diego R Faria Aston Robotics Vision and Intelligent Systems Laboratory (ARVIS Lab) College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Aston Institute of Photonic Technologies Aston University Birmingham B4 7ET UK

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.

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Correction: Gut microbiome in endometriosis: a cohort study on 1000 individuals

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BMC medicine 2024年第1期22卷 448页

作者： Inmaculada Pérez-Prieto Eva Vargas Eduardo Salas-Espejo Kreete Lüll Analuce Canha-Gouveia Laura Antequera Pérez Juan Fontes Andres Salumets Reidar Andreson Oliver Aasmets Katrine Whiteson Elin Org Signe Altmäe Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. inmapprieto@ugr.es. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. inmapprieto@ugr.es. Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. Systems Biology Unit Department of Experimental Biology Faculty of Experimental Sciences University of Jaen Jaen Spain. Institute of Genomics Estonian Genome Centre University of Tartu Tartu Estonia. Department of Physiology Faculty of Veterinary University of Murcia Campus Mare Nostrum Murcia Spain. Department of Computer Engineering Automation and Robotics University of Granada Granada Spain. U. Reproducción UGC Laboratorio Clínico y UGC Obstetricia y Ginecología. HU Virgen de Las Nieves Granada Spain. Competence Centre On Health Technologies Tartu Estonia. Division of Obstetrics and Gynaecology Department of Clinical Science Intervention and Technology Karolinska Institutet Huddinge Stockholm Sweden. Department of Gynaecology and Reproductive Medicine Karolinska University Hospital Huddinge Stockholm Sweden. Department of Obstetrics and Gynaecology Institute of Clinical Medicine University of Tartu Tartu Estonia. Institute of Molecular and Cell Biology University of Tartu Tartu Estonia. School of Biological Sciences University of California Irvine CA USA.

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Forecasting intracranial hypertension using multi-scale waveform metrics

arXiv

引用

arXiv 2019年

作者： Hüser, Matthias Kündig, Adrian Karlen, Walter De Luca, Valeria Jaggi, Martin Biomedical Informatics Group Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zürich Zürich8008 Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Machine Learning & Optimization Lab EPFL Lausanne1015 Switzerland

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time serie1s, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain. Copyright © 2019, The Authors. All rights reserved.

关键词： Morphology

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Decentralized controller design in flexure-linked gantry by H2 guaranteed cost optimization approach 18

Decentralized controller design in flexure-linked gantry by ...

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18th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2018

作者： Ma, Jun Chen, Si-Lu Liang, Wenyu Teo, Chek Sing Tay, Arthur Al Mamun, Abdullah Tan, Kok Kiong SIMTech-NUS Joint Lab. on Precision Motion Systems Department of Electrical and Computer Engineering National University of Singapore Singapore117582 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Agency for Science Technology and Research Singapore Institute of Manufacturing Technology Singapore138634 Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315201 China

ISBN: (纸本)9780995775121

Dual-drive H-gantry is widely used in many industrial processes that require high-speed and high-precision Cartesian motion. Unlike the rigid-linked gantry design, the flexure-linked design is able to prevent the damage of the cross-arm because a small degree of rotation angle is possible. However, the flexure-linked design may induce the resonant modes of the gantry because of high frequency control signals. To maintain the precision tracking of two carriages and minimize the chattering of control efforts, we aim to seek the most suitable flexure joints among the available ones while optimizing the decentralized feedback controller parameters under model uncertainties. In this way, good tracking performance is achieved without costly system re-design. In this work, we formulate the mechatronic design problem as an H2 guaranteed cost control problem. All the stabilizing feedback controller gains are parameterized over a convex set, and the global optimum is obtained by means of an outer-linearization-based algorithm. Experiments are conducted and the results successfully validate the effectiveness of the proposed design approach. © 2018 CURRAN-CONFERENCE. All rights reserved.

关键词： Convex optimization Dual-drive H-gantry Flexure Mechatronic design Optimal control Parallel mechanism

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A Four-Bank Memory Design of Optimized Semi-Parallel Polar Decoder 9

A Four-Bank Memory Design of Optimized Semi-Parallel Polar D...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Shiwen Xing Yun-Nan Chang Liang Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Department of Computer Science and Engineering Sun Yat-sen University

ISBN: (纸本)9781509038237;9781509038220

Polar code, which is capable of achieving channel capacity, is known as a promising forward error correction（FEC） code in coding theory. In this paper, a four-bank memory architecture is proposed for polar decoder to store the initial and the intermediate processing data. Based on the proposed load-balance data allocation scheme, each memory bank is able to be equipped with only two access ports, while providing enough data bandwidth demand. Besides, the proposed semi-parallel successive cancellation（SC） polar decoder is optimized by employing the efficient p node and the merged computation of f and g nodes to reduce the decoding latency, which can at most get 62.5% speed improvement with enough processing elements（PEs）. In addition, the proposed four-bank memory design can reduce the overall size of memory especially with higher level of parallelism.

关键词： ploar code successive cancellation decoding fourbank memory design optimized semi-parallel design

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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