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arXiv 2022年

作者： Sendra-Balcells, Carla Campello, Víctor M. Torrents-Barrena, Jordina Ahmed, Yahya Ali Elattar, Mustafa Ohene-Botwe, Benard Nyangulu, Pempho Stones, William Ammar, Mohammed Benamer, Lamya Nawal Kisembo, Harriet Nalubega Sereke, Senai Goitom Wanyonyi, Sikolia Z. Temmerman, Marleen Gratacós, Eduard Bonet, Elisenda Eixarch, Elisenda Mikolaj, Kamil Tolsgaard, Martin Grønnebæk Lekadir, Karim Dept. de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain HP Inc. Barcelona Spain Obstetrics and Gynecology Dept. School of Medicine Suez University Egypt Medical Imaging and Image Processing Center of Informatics Science Nile University Egypt Research & Development Division Intixel Egypt Dept. of Radiography School of Biomedical & Allied Health Sciences College of Health Sciences University of Ghana Ghana Division of Midwifery and Radiography School of Health & Psychological Sciences University of London London United Kingdom Kamuzu University of Health Sciences Blantyre Malawi Dept. of Electrical Engineering Systems Laboratory of Engineering System and Telecommunication University of M’Hamed Bougara Boumerdes Algiers Algeria Obstetrics and Gynecology Dept. School of Medicine Algiers University Algeria Dept. of Radiology Mulago National Referral and Teaching Hospital Kampala Uganda Dept. of Radiology and Radiotherapy School of Medicine Makerere University College of Health Sciences Kampala Uganda Dept. of Obstetrics and Gynaecology Aga Khan University Hospital 3rd Parklands Avenue Nairobi Kenya Centre of Excellence in Women and Child Health Aga Khan University Nairobi Kenya BCNatal Fetal Medicine Research Center Hospital Clínic and Hospital Sant Joan de Déu Universitat de Barcelona Barcelona Spain Barcelona Spain Barcelona Spain Barcelona Tech Universitat Politècnica de Catalunya Barcelona Spain Copenhagen Academy for Medical Education and Simulation Dept. of Obstetrics Rigshospitalet Denmark

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92±0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usabil

关键词： Ultrasonic imaging

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Medical Additive Manufacturing: From a Frontier Technology to the Research and development of Products

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Engineering 2020年第11期6卷 1217-1221页

作者： Guixing Qiu Wenjiang ding Wei Tian Ling Qin Yu Zhao Lianmeng Zhang Jian Lu daijie Chen Guangyi Yuan Chengtie Wu Bingheng Lu Ruxu du Jimin Chen Mo Elbestawi Zhongwei Gu dichen Li Wei Sun Yuanjin Zhao Jie He dadi Jin Bin Liu Kai Zhang Jianmo Li Kam WLeong dewei Zhao dingjun Hao Yingfang Ao Xuliang deng Huilin Yang ShaoKeh Hsu Yingqi Chen Long Li Jianping Fan Guohui Nie Yun Chen Hui Zeng Wei Chen Yuxiao Lai Department of Orthopedics Peking Union Medical College HospitalChinese Academy of Medical Science 8 Peking Union Medical CollegeBejjing 100730.China National Engineering Research Center of Light Alloys Net Forming&State Key Laboratony of Metal Matrix Composite Shanghai Jiao Tong UniversityShanghai 200240China Notional Engineering Research Center of Mg Materials and Applicarions and School of Materials Science 8r Engineering.Shanghai Jlao Tong University Shanghai 200240China Department of Spine Surgery Beijing Jishuitan HospitalBeijing 100035China Center for Troanslational Medicine Research and Development Institute of Biomedical and Health EngineeringShenzhen Insitute of Advanced Technology.Chinese Academy of SciencesShenzhen 518055China Guangdong Engineering Laboratony of Biomaterials Additive Manufacturing Guangdong 518055China Musculoskeletal Research Laboratory of the Department of Orthopedics 8 Traumatology and Innovative Orthopedic Biomaterial and Drug Translational Research Laboratory Li Ka Shing Institute of Health SciencesThe Chinese University of Hong KongHong Kong 999077.China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Center for Advanced Structural Materials City University of Hong Kong Shenzhen Research InstiuteShenzhen 518057.China Hong Kong Branch of National Precious Metals Material Engineering Research Center Department of Material Science and EngineeringCity Untiversity of Hong KongHong Kong 999077.China Department of Mechanical Engineering ity Untiversity of Hong Kong.Hong Kong 9977 China School of Pharmacy.Shanghai Jiao Tong University Shanghai 200240China China State Instute of Pharmaceutical Industry Shanghai Institute of Pharmaceutical IndustryShonghai 201203China Shanghai Innovation Instinute for Materials Shanghai 200444.China State Key Laboratory of High-Performance Ceramies and Superfine Microstructure Shanghai Institute of CeramicsChinese Acadermy 0f SciencesShanghai 200050Ch

*** and development(R&d)and the challenges of raw materials for medical additive manufacturing Raw materials for medical additive manufacturing have a wide range of commonalities that are also seen in many other fields,making them an important basis in the field of three-dimensional(3d)*** and challenges related to material types,powder properties,formability,viscoelasticity,and so forth also share common *** example,many metal materials are used in the field of aviation,while metals,polymers,and inorganic materials are used in the field of *** most widely used materials in biomedicine are *** homogeneous and non-homogeneous composites are also available for 3d printing,and impose an additional challenge in additive manufacturing;the use of heterogeneous composites in 3d printing is particularly challenging.

关键词： composites printing additive

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Single camera based 3d tracking for outdoor fall detection toward smart healthcare

Single camera based 3D tracking for outdoor fall detection t...

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International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Myeongseob Ko Suneung Kim Kyungchai Lee Mingi Kim Kwangtaek Kim Korea University Seongbuk-gu Seoul KR 3D Information Processing Laboratory Korea University Seoul Republic of Korea

detection of falling is an important research issue to develop smart healthcare technologies for elderly people as the population is rapidly increasing. To achieve the goal, accurate human tracking is imperative to detect a fall regardless of distance and occlusions. However, existing technologies are focusing on either 2d tracking with a single camera or 3d tracking using a depth camera (distance limitation) or multiple cameras (high complexity). In this paper, we introduce a new approach that combines Extended Kalman Filter (EKF) based 2d image tracking and 3d depth tracking to improve the performance of fall detection with a single camera. The experimental results demonstrate that our approach is a promising way of automatic fall detection that becomes a core technology of a smart healthcare solution for the elderly.

关键词： Cameras Three-dimensional displays Kalman filters Trajectory Two dimensional displays Wearable sensors

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A Temporally and Spatially Explicit, Mechanistic Estimation of Ragweed Pollen Concentrations Across Europe

SSRN

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SSRN 2023年

作者： Makra, László Matyasovszky, István Tusnády, Gábor Ziska, Lewis H. Hess, Jeremy J. Nyúl, László G. Chapman, daniel S. Coviello, Luca Gobbi, Andrea Jurman, Giuseppe Furlanello, Cesare Brunato, Mauro damialis, Athanasios Charalampopoulos, Athanasios Müller-Schärer, Heinz Schneider, Norbert Szabó, Bence Sümeghy, Zoltán Páldy, Anna Magyar, donát Bergmann, Karl-Christian deák, Áron József Mikó, Edit Thibaudon, Michel Oliver, Gilles Albertini, Roberto Bonini, Maira Šikoparija, Branko Radišić, Predrag Mitrović Josipović, Mirjana Gehrig, Regula Severova, Elena Rodinkova, Victoria Palamarchuk, Olena Stjepanović, Barbara Ianovici, Nicoleta Berger, Uwe Kofol Seliger, Andreja Rybníček, Ondřej Myszkowska, dorota dąbrowska-Zapart, Katarzyna Majkowska-Wojciechowska, Barbara Weryszko-Chmielewska, Elzbieta Grewling, Lukasz Rapiejko, Piotr Malkiewicz, Malgorzata Šaulienė, Ingrida Shalaboda, Valentina Prykhodo, Olexander Maleeva, Anna Ščevková, Jana Bullock, James M. Institute of Economics and Rural Development Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Department of Meteorology Eötvös Loránd University P.O.B. 32 Budapest1518 Hungary Alfréd Rényi Institute of Mathematics P.O.B 127 Budapest1364 Hungary Mailman School of Public Health Columbia University New YorkNY10032 United States Department of Global Health University of Washington SeattleWA98105 United States Department of Image Processing and Computer Graphics University of Szeged P.O.B. 652 Szeged6701 Hungary Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling Scotland StirlingFK9 4LA United Kingdom University of Trento Enogis s.r.l. Trento Italy Bruno Kessler Foundation Trento Italy HK3 Lab Orobix Life Srl Rovereto Bergamo Italy Department of Information Engineering and Computer Science University of Trento Trento Italy Terrestrial Ecology and Climate Change Department of Ecology School of Biology Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Departement of Biology Unit of Ecology and Evolution University of Fribourg FribourgCH-1700 Switzerland National Institute of Environmental Health Albert Flórián út 2-6 Budapest1097 Hungary Institute of Allergology Charité Hindenburgdamm 30 Berlin12203 Germany Institute of Animal Science and Wildlife Management Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Réseau National de Surveillance Aérobiologique 11 chemin de la Creuzille Le Plat du Pin Brussieu696905 France Laboratory of Hygiene and Aerobiology Department of Medicine and Surgery University of Parma U.O. Medicina Interna di Continuità Azienda Ospedaliero-Universitaria di Parma Via Gramsci 14 Parma43126 Italy Hygiene and Public Health Service via Spagliardi 19 Parabiago Milan20015 Italy BioSensе Institute Research Institute for Information Technologies in Biosystems University of Novi Sad Dr

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we developed two statistical procedures, a Gaussian method (GM) and deep learning (dL) for restoring missing daily ragweed pollen data sets, based on the plant’s reproductive and growth (phenological, pollen production and frost-related) characteristics. dL model performances were consistently better for estimating annual total pollen concentrations than those of the GM approach. These are the first published modeled maps using altitude correction and phenology to recover missing pollen information. We created a web page (http://***/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions. © 2023, The Authors. All rights reserved.

关键词： Climate change

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医用增材制造——从前沿技术到产品研发

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Engineering 2020年第11期6卷 31-41页

作者：邱贵兴丁文江田伟秦岭赵宇张联盟吕坚陈代杰袁广银吴成铁卢秉恒杜如虚陈继明 Mo Elbestawi 顾忠伟李涤尘孙伟赵远锦赫捷金大地刘斌张凯李鉴墨 Kam W.Leong 赵德伟郝定均敖英芳邓旭亮杨惠林徐少克陈英奇李龙樊建平聂国辉陈芸曾晖陈玮赖毓霄 Department of Orthopedics Peking Union Medical College Hospital Chinese Academy of Medical Science & Peking Union Medical College National Engineering Research Center of Light Alloys Net Forming & State Key Laboratory of Metal Matrix Composite Shanghai Jiao Tong University National Engineering Research Center of Mg Materials and Applications and School of Materials Science & Engineering Shanghai Jiao Tong University Department of Spine Surgery Beijing Jishuitan Hospital Center for Translational Medicine Research and Development Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Engineering Laboratory of Biomaterials Additive Manufacturing State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Center for Advanced Structural Materials City University of Hong Kong Shenzhen Research Institute Hong Kong Branch of National Precious Metals Material Engineering Research Center Department of Material Science and Engineering City University of Hong Kong Department of Mechanical Engineering City University of Hong Kong School of Pharmacy Shanghai Jiao Tong University China State Institute of Pharmaceutical Industry Shanghai Institute of Pharmaceutical Industry State Key Laboratory of High-Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of the Chinese Academy of Sciences Micro- and Nano-Technology Research Center State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Shien-Ming Wu School of Intelligent Engineering Guangzhou International Campus South China University of Technology Beijing Engineering Research Center of 3D Printing for Digital Medical Health Beijing University of Technology Beijing Engineering Research Center of Laser Technology Beijing University of Technology Key Laboratory of Trans-Scale Las

1.医用增材制造原料的研发和挑战医用增材制造的原料与其他多个领域使用的原料具有广泛通用性,这是构成三维(3d)打印领域的重要基础。这些领域的增材制造在材料类型、粉体特性、成型性和黏弹性等方面面临着相同的问题和挑战。例如,航空...

1.医用增材制造原料的研发和挑战医用增材制造的原料与其他多个领域使用的原料具有广泛通用性,这是构成三维(3d)打印领域的重要基础。这些领域的增材制造在材料类型、粉体特性、成型性和黏弹性等方面面临着相同的问题和挑战。例如,航空领域采用了多种金属材料,而生物医学领域会使用金属、聚合物和无机材料。在生物医学领域,

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Photoluminescence Enhancement: Over 1000-Fold Enhancement of the Unidirectional Photoluminescence from a Microsphere-Cavity-Array-Capped Qd/PdMS Composite Film for Flexible Lighting and displays (Advanced Optical Materials 24/2019)

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Advanced Optical Materials 2019年第24期7卷

作者： Lixue Yang Lin Li Qiang Wang Cheng Xing Lin Ma Yong Zeng Yan Zhao Yinzhou Yan Institute of Laser Engineering Beijing University of Technology Beijing 100124 China Laser Processing Research Centre School of Mechanical Aerospace and Civil Engineering The University of Manchester Manchester M13 9PL UK Institute of Microstructure and Property of Advanced Materials Beijing University of Technology Beijing 100124 China Beijing Engineering Research Center of 3D Printing for Digital Medical Health Beijing University of Technology Beijing 100124 China Beijing Engineering Research Center of Laser Technology Beijing University of Technology Beijing 100124 China Key Laboratory of Trans-scale Laser Manufacturing Ministry of Education Beijing University of Technology Beijing 100124 China

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Bioprinting: Aqueous Two‐Phase Emulsion Bioink‐Enabled 3d Bioprinting of Porous Hydrogels (Adv. Mater. 50/2018)

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Advanced Materials 2018年第50期30卷

作者： Guo‐Liang Ying Nan Jiang Sushila Maharjan Yi‐Xia Yin Rong‐Rong Chai Xia Cao Jing‐Zhou Yang Amir K. Miri Shabir Hassan Yu Shrike Zhang Division of Engineering in Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School Cambridge MA 02139 USA School of Materials Science and Engineering Wuhan Institute of Technology Wuhan 430205 P.R. China School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Research Institute for Bioscience and Biotechnology Lalitpur 44600 Nepal State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan 430070 P.R. China Center of Biomedical Materials 3D Printing National Engineering Laboratory for Polymer Complex Structure Additive Manufacturing Baoding 071000 P.R. China

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RELICS: Strong lensing analysis of MACSJ0417.5−1154 and predictions for observing the magnified high-redshift universe with JWST

arXiv

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arXiv 2018年

作者： Mahler, Guillaume Sharon, Keren Fox, Carter Coe, dan Jauzac, Mathilde Strait, Victoria Edge, Alastair Acebron, Ana Andrade-Santos, Felipe Avila, Roberto J. Bradač, Maruša Bradley, Larry d. Carrasco, daniela Cerny, Catherine Cibirka, Nathália Czakon, Nicole G. dawson, William A. Frye, Brenda L. Hoag, Austin T. Huang, Kuang-Han Johnson, Traci L. Jones, Christine Kikuchihara, Shotaro Lam, daniel Livermore, Rachael Lovisari, Lorenzo Mainali, Ramesh Ogaz, Sara Ouchi, Masami Paterno-Mahler, Rachel Roederer, Ian U. Ryan, Russell E. Salmon, Brett Sendra-Server, Irene Stark, daniel P. Toft, Sune Trenti, Michele Umetsu, Keiichi Vulcani, Benedetta Zitrin, Adi Department of Astronomy University of Michigan 1085 South University Ave Ann ArborMI48109 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Centre for Extragalactic Astronomy Department of Physics Durham University DurhamDH1 3LE United Kingdom Institute for Computational Cosmology Durham University South Road DurhamDH1 3LE United Kingdom Astrophysics and Cosmology Research Unit School of Mathematical Sciences University of KwaZulu-Natal Durban4041 South Africa Department of Physics University of California DavisCA95616 United States Physics Department Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva84105 Israel Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States School of Physics University of Melbourne VIC3010 Australia Astronomy Department Institute for Astrophysical Research Boston University 725 Commonwealth Avenue BostonMA02215 United States Institute of Astronomy and Astrophysics Academia Sinica PO Box 23-141 Taipei10617 Taiwan Lawrence Livermore National Laboratory P.O. Box 808 L-210 LivermoreCA94551 United States Department of Astronomy Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Department of Astronomy University of Michigan 1085 South University Drive Ann ArborMI48109 United States Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba277-8582 Japan Leiden Observatory Leiden University LeidenNL-2300 RA Netherlands ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions ASTRO 3D VIC2010 Australia Kavli Institute Physics and Mathematics of the Universe Kavli IPMU WPI University of Tokyo Chiba277-8582 Japan United States Infrared Processing and Analysis Center California Institute of Technology MS 100-22 PasadenaCA91125 United States Cosmic Dawn Center Niels Bohr Institute University of Copenhagen Juliane Maries

Strong gravitational lensing by clusters of galaxies probes the mass distribution at the core of each cluster and magnifies the universe behind it. MACS J0417.5−1154 at z = 0.443 is one of the most massive clusters known based on weak lensing, X-ray, and Sunyaev-Zel'dovich analyses. Here we compute a strong lens model of MACS J0417 based on Hubble Space Telescope imaging observations collected, in part, by the Reionization Lensing Cluster Survey (RELICS), and recently reported spectroscopic redshifts from the MUSE instrument on the Very Large Telescope (VLT). We measure an Einstein radius of θE '3600 at z = 9 and a mass projected within 200 kpc of M(200 kpc) = 1.78+0−0..0103 × 1014Mo. Using this model, we measure a ratio between the mass attributed to cluster-member galaxy halos and the main cluster halo of order 1:100. We assess the probability to detect magnified high-redshift galaxies in the field of this cluster, both for comparison with RELICS HST results and as a prediction for the James Webb Space Telescope (JWST) Guaranteed Time Observations upcoming for this cluster. Our lensing analysis indicates that this cluster has similar lensing strength to other clusters in the RELICS program. Our lensing analysis predicts a detection of at least a few z ∼ 6 − 8 galaxies behind this cluster, at odds with a recent analysis that yielded no such candidates in this field. Reliable strong lensing models are crucial for accurately predicting the intrinsic properties of lensed galaxies. As part of the RELICS program, our strong lensing model produced with the Lenstool parametric method is publicly available through the Mikulski Archive for Space Telescopes (MAST). Copyright © 2018, The Authors. All rights reserved.

关键词： Galaxies

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Oscillations on width and intensity of slender Ca II H fibrils from SUNRISE/sufi

arXiv

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arXiv 2017年

作者： Gafeira, R. Jafarzadeh, S. Solanki, S.K. Lagg, A. van Noort, M. Barthol, P. Blanco Rodríguez, J. del Toro Iniesta, J.C. Gandorfer, A. Gizon, L. Hirzberger, J. Knölker, M. Orozco Suárez, d. Riethmüller, T.L. Schmidt, W. Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern OsloN-0315 Norway School of Space Research Kyung Hee University Yongin Gyeonggi446-701 Korea Republic of Image Processing Laboratory University of Valencia P.O. Box 22085 Paterna ValenciaE-46980 Spain Apartado de Correos 3004 GranadaE-18080 Spain Institut für Astrophysik Georg-August-Universität Göttingen Friedrich-Hund-Platz 1 Göttingen37077 Germany High Altitude Observatory National Center for Atmospheric Research P.O. Box 3000 BoulderCO80307-3000 United States Kiepenheuer-Institut für Sonnenphysik Schöneckstr. 6 FreiburgD-79104 Germany

We report the detection of oscillations in slender Ca II H fibrils (SCFs) from high-resolution observations acquired with the SUNRISE balloon-borne solar observatory. The SCFs show obvious oscillations in their intensity, but also their width. The oscillatory behaviors are investigated at several positions along the axes of the SCFs. A large majority of fibrils show signs of oscillations in intensity. Their periods and phase speeds are analyzed using a wavelet analysis. The width and intensity perturbations have overlapping distributions of the wave period. The obtained distributions have median values of the period of 32 ± 17 s and 36 ± 25 s, respectively. We find that the fluctuations of both parameters propagate in the SCFs with speeds of 11+−4911 km/s and 15+−3415 km/s, respectively. Furthermore, the width and intensity oscillations have a strong tendency to be either in anti-phase, or, to a smaller extent, in phase. This suggests that the oscillations of both parameters are caused by the same wave mode and that the waves are likely propagating. Taking all the evidence together, the most likely wave mode to explain all measurements and criteria is the fast sausage mode. Copyright © 2017, The Authors. All rights reserved.

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass david Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu desrosiers Simona Brambati Julien doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander david Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco de Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro daducci Tim B dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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