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Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology (vol 13, 634, 2022)

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

作者： Bjornsdottir, Gyda Stefansdottir, Lilja Thorleifsson, Gudmar Sulem, Patrick Norland, Kristjan Ferkingstad, Egil Oddsson, Asmundur Zink, Florian Lund, Sigrun H. Nawaz, Muhammad S. Walters, G. Bragi Skuladottir, Astros Th. Gudjonsson, Sigurjon A. Einarsson, Gudmundur Halldorsson, Gisli H. Bjarnadottir, Valgerdur Sveinbjornsson, Gardar Helgadottir, Anna Styrkarsdottir, Unnur Gudmundsson, Larus J. Pedersen, Ole B. Hansen, Thomas Folkmann Werge, Thomas Banasik, Karina Troelsen, Anders Skou, Soren T. Thorner, Lise Wegner Erikstrup, Christian Nielsen, Kaspar Rene Mikkelsen, Susan Jonsdottir, Ingileif Bjornsson, Aron Olafsson, Ingvar H. Ulfarsson, Elfar Blondal, Josep Vikingsson, Arnor Brunak, Soren Ostrowski, Sisse R. Ullum, Henrik Thorsteinsdottir, Unnur Stefansson, Hreinn Gudbjartsson, Daniel F. Thorgeirsson, Thorgeir E. Stefansson, Kari deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland Faculty of Medicine School of Health Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland School of Engineering and Natural Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) Landspitali University Hospital Reykjavik Iceland (GRID:grid.410540.4) (ISNI:0000 0000 9894 0842) Zealand University Hospital Department of Clinical Immunology Køge Denmark (GRID:grid.512923.e) (ISNI:0000 0004 7402 8188) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Rigshospitalet-Glostrup Danish Headache Center Dept. Neurology Glostrup Denmark (GRID:grid.475435.4) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Copenhagen University Hospital Institute of Biological Psychiatry Mental Health Services Copenhagen Denmark (GRID:grid.4973.9) (ISNI:0000 0004 0646 7373) University of Copenhagen Lundbeck Foundation for GeoGenetics GLOBE Institute Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001

Back pain is a common and debilitating disorder with largely unknown underlying biology. Here we report a genome-wide association study of back pain using diagnoses assigned in clinical practice; dorsalgia (119,100 cases, 909,847 controls) and intervertebral disc disorder (IDD) (58,854 cases, 922,958 controls). We identify 41 variants at 33 loci. The most significant association (ORIDD = 0.92, P = 1.6 × 10−39; ORdorsalgia = 0.92, P = 7.2 × 10−15) is with a 3’UTR variant (rs1871452-T) in CHST3, encoding a sulfotransferase enzyme expressed in intervertebral discs. The largest effects on IDD are conferred by rare (MAF = 0.07 − 0.32%) loss-of-function (LoF) variants in SLC13A1, encoding a sodium-sulfate co-transporter (LoF burden OR = 1.44, P = 3.1 × 10−11); variants that also associate with reduced serum sulfate. Genes implicated by this study are involved in cartilage and bone biology, as well as neurological and inflammatory *** is known about the biology of back pain, a leading cause of disability. Here the authors report 30 new back pain loci, implicating genes involved in cartilage/bone biology, as well as neurological and inflammatory processes.

关键词： Bone Genome-wide association studies Pain

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Efficient construction method for phase diagrams using uncertainty sampling

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Physical Review Materials 2019年第3期3卷 033802-033802页

作者： Kei Terayama Ryo Tamura Yoshitaro Nose Hidenori Hiramatsu Hideo Hosono Yasushi Okuno Koji Tsuda RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan Medical Sciences Innovation Hub Program RIKEN Cluster for Science Technology and Innovation Hub Kanagawa 230-0045 Japan Graduate School of Medicine Kyoto University Kyoto 606-8507 Japan International Center for Materials Nanoarchitectonics (WPI-MANA) National Institute for Materials Science Ibaraki 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki 305-0047 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8568 Japan Department of Materials Science and Engineering Kyoto University Kyoto 606-8501 Japan Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology Yokohama 226-8503 Japan Materials Research Center for Element Strategy Tokyo Institute of Technology Yokohama 226-8503 Japan

We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phases can be rapidly found, and smaller numbers of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science.

关键词： Phase diagrams

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A Study of Combined Peak-Searching Method for Symmetric Zero Area and Gaussian Product Function Based on γ-ray Spectrum

A Study of Combined Peak-Searching Method for Symmetric Zero...

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2018 4th Workshop on Advanced research and technology in Industry Applications(WARTIA 2018)

作者： He Jianfeng Huang Sheng Ye Zhixiang Wang Xueyuan Li Xiang Zhou Shirong Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China Institute of Technology Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology East China University of Technology Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Jiangxi Food and Drug Administration

The peak position of each peak is determined accurately in the spectra, which is a key problem in the analysis of γ-ray spectrum. In this paper, basis of comparing the Gaussian product function method and the symmetrical zero area method, the IAEA-approved G-1200 is selected as the peak-searching test spectra, the simulation experiment based on MATLAB is used for the test analysis and to extract the corresponding characteristic parameters of the spectral line. Aiming at the respective advantages of the two peak searching methods in γ-ray spectrum analysis, a method of combined peak-searching for symmetric zero area and Gaussian product function is proposed, which the statistical fluctuation of spectral data is effectively reduced, the background effect is eliminated, the peak positions information is highlighted, and the true peaks or false peaks can be identified according to the preset conditions. The result of experiment represents that symmetric zero area and Gaussian product function combined peak-searching method to peak-searching is also suitable for unsmooth data, this method has the advantages of small calculation amount, fast searching speed and obvious effect.

关键词： γ-ray Energy Spectra Gaussian Product Function Method Symmetric Zero Area Method Combined Peak-Searching

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New parameter-free mobility model: Opportunity priority selection model

arXiv

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arXiv 2018年

作者： Liu, Erjian Yan, Xiao-Yong Key Lab. of Integrated Transportation Big Data Application Technology for Transportation Industry Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China

Predicting human mobility patterns has many practical applications in urban planning, traffic engineering, infectious disease epidemiology, emergency management and location-based services. Developing a universal model capable of accurately predicting the mobility fluxes between locations is a fundamental and challenging problem in regional economics and transportation science. Here, we propose a new parameter-free model named opportunity priority selection model as an alternative in human mobility prediction. The basic assumption of the model is that an individual will select destination locations that present higher opportunity benefits than the location opportunities of the origin and the intervening opportunities between the origin and destination. We use real mobility data collected from a number of cities and countries to demonstrate the predictive ability of this simple model. The results show that the new model offers universal predictions of intracity and intercity mobility patterns that are consistent with real observations, thus suggesting that the proposed model better captures the mechanism underlying human mobility than previous models. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Global climate

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Bulletin of the American Meteorological Society 2020年第8期101卷 S9-S128页

作者： Dunn, R.J.H. Stanitski, D.M. Gobron, N. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom NOAA/OAR Earth System Research Laboratories Boulder Colorado European Commission Joint Research Centre Ispra Italy Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Met Office Hadley Centre Exeter United Kingdom University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton Virginia NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina University of Bremen Bremen Germany NOAA/OAR Earth System Research Laboratories Boulder Colorado Centro de Investigaciones sobre Desertificación - Spanish National Research Council Moncada (Valencia) Spain and Regional Climate Group Department of Earth Sciences University of Gothenburg Gothenburg Sweden Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile NOAA/OAR ESRL Global Monitoring Laboratory Boulder Colorado Department of Civil and Environmental Engineering Princeton University Princeton New Jersey Global Precipitation Climatology Centre Deutscher Wetterdienst Offenbach Germany University of Reading Reading United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom National Oceanography Centre Southampton United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Institut de physique du globe de Paris CNRS IGN Paris France and ENSG-Géomatique IGN Marne-la-Vallée France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Sorbonne Université Paris France Universität Hamburg Hamburg Germany Department of Meteorology University of Reading Reading United Kingdom Geology Department University Centre in Svalbard Longyearbyen Norway Jet Propulsion Laboratory California Institute of Technology Wrightwood Calif

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Classification of late-stage wheat powdery mildew based on Isomap and PNN analyses

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International Agricultural engineering Journal 2018年第4期 404-410页

作者： Huang, Linsheng Wang, Yulong Guo, Wei Zhang, Qing Yang, Xiaodong National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Beijing Research Center for Information Technology in Agriculture Beijing100097 China College of Information and Management Science Henan Agricultural University Zhengzhou450002 China

In order to provide assistance in precisely distinguishing different severities of wheat powdery mildew, imaging hyperspectral data of disease-infected and healthy wheat leaves were collected in the late growth stage. After conversion and enhancement of hyperspectral data were preprocessed, the isometric feature mapping (Isomap) was used to reduce the original normalized spectral data from 178 samples. Of these, 136 samples were used as training samples, and the rest were seen as test samples. The characteristics of the reduced dimension data were input into a probabilistic neural network (PNN). A model of classification and recognition was established, resulting in 100% prediction accuracy for healthy samples, 90% for samples with moderate diseased infection, and 80% for samples with mild and severe disease infection, and an overall recognition accuracy of 88% for the model. These results provide important theoretical basis for accurately real-time disease assessment, and also guide the agricultural production and disease prevention. © 2018, Asian Association for Agricultural engineering. All rights reserved.

关键词： Neural networks

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Application and development of elastic fibers focusing on composition and structure

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

作者： Jin Zhang Qinyue Sun Yumin Wang Xiongwei Dong Yi Xiong Wei Zeng Department of Pharmacy Zhongnan Hospital Wuhan University Wuhan 430071 People's Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application School of Electronics and Information Engineering Anhui University Hefei 230601 Anhui People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro-and Nano-Structures Wuhan University Wuhan 430072 People's Republic of China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Wuhan 430073 Hubei People's Republic of China

Elastic fabric is comfortable to wear, and has been a consumer textile due to its good shape retention and wrinkle resistance after washing. This paper briefly introduces the types and performance characteristics of several common elastic fibers, and analyzes the relationship between the structure and elasticity of different elastic fibers. Finally, the development trend of elastic fibers is proposed.

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Small P values may not yield robust findings:an example using REST-meta-PD

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Science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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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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