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2017 ACM SIGCHI Conference on Human Factors in Computing systems, CHI EA 2017

作者： Zamansky, Anna Roshier, Amanda Mancini, Clara Collins, Emily C. Hall, Carol Grillaert, Katie Morrison, Ann North, Steve Wirman, Hanna Information Systems Department University of Haifa Israel School of Veterinary Medicine and Science University of Nottingham United Kingdom Animal-Computer Interaction Lab Open University Milton Keynes United Kingdom Sheffield Robotics University of Sheffield United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University United Kingdom Messerli Research Institute University of Veterinary Medicine Medical University of Vienna University of Vienna Austria Department of Architecture Design and Media Technology Aalborg University Denmark Mixed Reality Laboratory University of Nottingham United Kingdom School of Design Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781450346566

Animal-computer Interaction (ACI) is a new and quickly developing discipline, which is closely related to HCI and is making reference to some of its theoretical frameworks and research methodologies. The first edition of the Workshop on research Methods in ACI (RM4ACI) was co-located with the Third International Conference on Animal-computer Interaction, which took place in Milton-Keynes, UK in November 2016. This paper presents an overview of the workshop, including insights from discussions on some of the challenges faced by the ACI community as it works to develop ACI as a discipline, and on important opportunities for cross-fertilization between HCI and ACI that the HCI community could consider. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Animals

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Author Correction: Observation of an ultra-high-energy cosmic neutrino with KM3NeT

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Nature 2025年第8057期640卷 E3页

作者： S. Aiello R. Bruno G. Ferrara E. Leonora F. Longhitano N. Randazzo A. Sinopoulou I. Tosta e Melo A. Albert D. Drouhin T. Pradier A. R. Alhebsi S. Thoudam A. van Vliet M. Alshamsi V. Bertin M. Breuhaus J. Brunner J. Busto L. Cerisy P. Coyle B. De Martino D. Dornic J.-G. Ducoin A. Enzenhöfer S. Henry D. Joly S. Lancelin S. Le Stum N. Lumb F. Magnani C. Paries M. Perrin-Terrin G. Vannoye S. Alves Garre A. Bariego-Quintana D. Calvo V. Cecchini A. Garcia Soto S. R. Gozzini J. J. Hernández-Rey A. Lazo N. Lessing J. Palacios González E. J. Pastor Gomez J. Prado D. Real A. Šaina F. Salesa Greus A. Sánchez Losa J. D. Zornoza J. Zúñiga A. Ambrosone M. Bendahman M. Benhassi C. Bozza R. M. Bozza R. Buompane A. Condorelli A. De Benedittis L. A. Fusco L. Gialanella W. Idrissi Ibnsalih A. Manfreda A. Marinelli F. Marzaioli S. Mastroianni G. Miele P. Migliozzi M. L. Mitsou C. M. Mollo L. Morales-Gallegos M. R. Musone V. Oliviero C. Poirè I. C. Rea A. Simonelli B. Spisso D. Vivolo F. Ameli A. Capone S. Celli I. Di Palma S. Gagliardini M. Mastrodicasa C. A. Nicolau A. Tudorache A. Veutro A. Zegarelli M. Andre M. Anghinolfi F. Badaracco B. Caiffi V. Ellajosyula C. Guidi V. Kulikovskiy L. Malerba M. Marconi B. Ó Fearraigh V. Parisi A. Romanov M. Sanguineti M. Taiuti S. Zavatarelli L. Aphecetche F. Bretaudeau R. Dallier V. Decoene L. Martin M. Ardid S. Ardid J. García Méndez C. A. Quiroz-Rangel C. Argüelles N. Kamp A. Y. Wen H. Atmani J. Boumaaza A. Eddymaoui Y. Tayalati J. Aublin B. Baret Y. Becherini T. Cartraud J. A. B. Coelho A. Coleiro S. Colonges A. Creusot C. Donzaud M. Eff S. El Hedri I. Goos P. Goswami A. Ilioni A. Kouchner M. Lindsey Clark E. Oukacha S. Peña Martínez R. Pestes A. Sharma B. Trocmé V. Van Elewyck C. Verilhac I. Lhenry-Yvon L. Bailly-Salins M. Bennani B. Guillon M. Labalme C. Lastoria G. Lehaut I. Mozun Mateo V. Pestel A. Vacheret Z. Bardačová E. Eckerová F. Mamedov Y. Shitov I. Štekl R. Dvornický F. Šimkovic F. Benfenati Gualandi F. Carenini P. Castaldi T. Chiarusi I. Del Rosso G. Levi A. Margiotta R. Muller INFN Sezione di Catania (INFN-CT) Catania Italy INFN Laboratori Nazionali del Sud (LNS) Catania Italy Dipartimento di Fisica e Astronomia “Ettore Majorana” Università di Catania (INFN-CT) Catania Italy Université de Strasbourg CNRS IPHC UMR 7178 Strasbourg France Université de Haute Alsace Mulhouse France Department of Physics Khalifa University Abu Dhabi United Arab Emirates Aix Marseille Université CNRS/IN2P3 CPPM Marseille France IFIC - Instituto de Física Corpuscular (CSIC - Universitat de València) Paterna Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli Italy Università di Napoli “Federico II” Dip. Scienze Fisiche “E. Pancini” Complesso Universitario di Monte S. Angelo Napoli Italy Dipartimento di Matematica e Fisica Università degli Studi della Campania “Luigi Vanvitelli” Caserta Italy Dipartimento di Fisica Università di Salerno e INFN Gruppo Collegato di Salerno Fisciano Italy INFN Sezione di Roma Roma Italy Dipartimento di Fisica Università La Sapienza Roma Italy Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Universitat Politècnica de Catalunya Vilanova i la Geltrú Spain INFN Sezione di Genova Genova Italy Università di Genova Genova Italy LPC CAEN Normandie Université ENSICAEN UNICAEN CNRS/IN2P3 Caen France Subatech IMT Atlantique IN2P3-CNRS Nantes Université Nantes France Instituto de Investigación para la Gestión Integrada de las Zonas Costeras Universitat Politècnica de València Gandia Spain Department of Physics Harvard University Cambridge MA USA Laboratory for Particle Physics and Cosmology Lyman Laboratory Harvard University Cambridge MA USA Faculty of Sciences University Mohammed V in Rabat Rabat Morocco Institute of Applied Physics Mohammed VI Polytechnic University Ben Guerir Morocco Astroparticule et Cosmologie Université Paris Cité CNRS Paris France Institut Universitaire de France Paris France Institute of Experimental and Applied Physics

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Studying bioluminescence flashes with the ANTARES deep Sea neutrino telescope

arXiv

引用

arXiv 2021年

作者： Reeb, Nico Hutschenreuter, S. Zehetner, P. Ensslin, T. Albert, A. Alves, S. André, M. Anghinolfi, M. Anton, G. Ardid, M. Aubert, J.-J. Aublin, J. Baret, B. Basa, S. Belhorma, B. Bendahman, M. Bertin, V. Biagi, S. Bissinger, M. Boumaaza, J. Bouta, M. Bouwhuis, M.C. Brânzaş, H. Bruijn, R. Brunner, J. Busto, J. Caiffi, B. Capone, A. Caramete, L. Carr, J. Carretero, V. Celli, S. Chabab, M. Chau, T.N. Chiarusi, T. Circella, M. Coleiro, A. Colomer-Molla, M. Coniglione, R. Coyle, P. Creusot, A. Díaz, A.F. de Wasseige, G. Deschamps, A. Distefano, C. Di Palma, I. Domi, A. Donzaud, C. Dornic, D. Drouhin, D. van Eeden, T. Enzenhöfer, A. Fermani, P. Ferrara, G. Filippini, F. Fusco, L. Gatelet, Y. Gay, P. Glotin, H. Gozzini, R. Ruiz, R. Gracia Graf, K. Guidi, C. Hallmann, S. van Haren, H. Heijboer, A.J. Hello, Y. Hernández-Rey, J.J. Hößl, J. Hofestädt, J. Huang, F. Illuminati, G. James, C.W. Jisse-Jung, B. de Jong, M. de Jong, P. Jongen, M. Kadler, M. Kalekin, O. Katz, U. Khan-Chowdhury, N.R. Kouchner, A. Kreykenbohm, I. Kulikovskiy, V. Lahmann, R. Le Breton, R. Lefèvre, D. Leonora, E. Levi, G. Lincetto, M. Lopez-Coto, D. Loucatos, S. Maderer, L. Manczak, J. Marcelin, M. Margiotta, A. Marinelli, A. Martínez-Mora, J.A. Melis, K. Migliozzi, P. Moussa, A. Muller, R. Nauta, L. Navas, S. Nezri, E. Fearraigh, B.Ó. Organokov, M. Pavalaş, G.E. Pellegrino, C. Perrin-Terrin, M. Piattelli, P. Pieterse, C. Poirè, C. Popa, V. Pradier, T. Randazzo, N. Reck, S. Riccobene, G. Romanov, A. Sánchez-Losa, A. Greus, F. Salesa Samtleben, D.F.E. Sanguineti, M. Sapienza, P. Schnabel, J. Schumann, J. Schüssler, F. Spurio, M. Stolarczyk, Th. Taiuti, M. Tingay, S.J. Vallage, B. van Elewyck, V. Versari, F. Viola, S. Vivolo, D. Wilms, J. Zavatarelli, S. Zegarelli, A. Zornoza, J.D. Zúñiga, J. Information Field Theory Group Max Planck Institute for Astrophysics Garching Germany Informatics 6 Chair of Robotics Artificial Intelligence and Real time Systems Technical University of Munich Garching Germany Department of Astrophysics IMAPP Radboud University Nijmegen Nijmegen Netherlands Department of Physics Ludwig-Maximilians University Munich Germany CERN Geneva Switzerland Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace MulhouseF-68200 France IFIC - Instituto de Física Corpuscular CSIC Universitat de València c/ Catedrático José Beltrán 2 Paterna ValenciaE-46980 Spain Technical University of Catalonia Laboratory of Applied Bioacoustics Rambla Exposició Vilanova i la Geltrú Barcelona08800 Spain INFN - Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Centre for Astroparticle Physics Erwin-Rommel-Str. 1 Erlangen91058 Germany Universitat Politècnica de València C/ Paranimf 1 Gandia46730 Spain Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université de Paris CNRS Astroparticule et Cosmologie ParisF-75013 France Aix Marseille Univ CNRS CNES LAM Marseille France National Center for Energy Sciences and Nuclear Techniques B.P.1382 RabatR. P. 10001 Morocco University Mohammed V in Rabat Faculty of Sciences 4 av. Ibn Battouta B.P. 1014 R.P. Rabat10000 Morocco Via S. Sofia 62 Catania95123 Italy University Mohammed I Laboratory of Physics of Matter and Radiations B.P.717 Oujda6000 Morocco Nikhef Science Park Amsterdam Netherlands Institute of Space Science Bucharest MagureleRO-077125 Romania Universiteit van Amsterdam Instituut voor Hoge-Energie Fysica Science Park 105 Amsterdam1098 XG Netherlands INFN - Sezione di Roma P.le Aldo Moro 2 Roma00185 Italy Dipartimento di Fisica Università La Sapienza P.le Aldo Moro 2 Roma00185 Italy LPHEA Faculty of Science - Semlali Cadi Ayyad University P.O

We develop a novel technique to exploit the extensive data sets provided by underwater neutrino telescopes to gain information on bioluminescence in the deep sea. The passive nature of the telescopes gives us the unique opportunity to infer information on bioluminescent organisms without actively interfering with them. We propose a statistical method that allows us to reconstruct the light emission of individual organisms, as well as their location and movement. A mathematical model is built to describe the measurement process of underwater neutrino telescopes and the signal generation of the biological organisms. The Metric Gaussian Variational Inference algorithm is used to reconstruct the model parameters using photon counts recorded by the neutrino detectors. We apply this method to synthetic data sets and data collected by the ANTARES neutrino telescope. The telescope is located 40 km off the French coast and fixed to the sea floor at a depth of 2475 m. The runs with synthetic data reveal that we can reliably model the emitted bioluminescent flashes of the organisms. Furthermore, we find that the spatial resolution of the localization of light sources highly depends on the configuration of the telescope. Precise measurements of the efficiencies of the detectors and the attenuation length of the water are crucial to reconstruct the light emission. Finally, the application to ANTARES data reveals the first precise localizations of bioluminescent organisms using neutrino telescope data. © 2021, CC BY.

关键词： Bioluminescence

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Dynamic topologies for particle swarms 7

Dynamic topologies for particle swarms

引用

7th International Joint Conference on Computational Intelligence, IJCCI 2015

作者： Fernandes, Carlos M. Laredo, J.L.J. Merelo, J.J. Cotta, C. Rosa, A.C. LARSyS: Laboratory for Robotics and Systems in Engineering and Science University of Lisbon Lisbon Portugal Department of Architecture and Computer Technology University of Granada Granada Spain Departamento de Lenguages y Ciencias de la Computación University of Malaga Malaga Spain

ISBN: (纸本)9783662535240

The Particle Swarm Optimization (PSO) algorithm is a population-based metaheuristics in which the individuals communicate through decentralized networks. The network can be of many forms but traditionally its structure is predetermined and remains fixed during the search. This paper investigates an alternative approach. The particles are positioned on a 2-dimensional grid of nodes. During the run, they move through the network according to simple rules, while interacting with each other using signs that they leave on the nodes. The links between the particles – and consequently the information flow – are then defined at each time step by the position of the particle on the grid. As a result, each particle’s set of neighbors and connectivity degree varies during the search progress. The particles can move randomly or instead track signs left by other particles on the grid. In this paper, after a formal description of the general model, two different strategies (random and sign-based) are tested and compared to standard topologies on unimodal and multimodal functions, including a rotated and a shifted function with noise from the CEC benchmark. The experiments demonstrate that the dynamics provided by the proposed structure results in a more consistent and stable performance throughout the test set. The working mechanisms of the model are simple and easy to implement. © Springer-Verlag Berlin Heidelberg 2016.

关键词： Topology

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DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self

arXiv

引用

arXiv 2017年

作者： Moulin-Frier, Clément Fischer, Tobias Petit, Maxime Pointeau, Grégoire Puigbo, Jordi-Ysard Pattacini, Ugo Low, Sock Ching Camilleri, Daniel Nguyen, Phuong Hoffmann, Matej Chang, Hyung Jin Zambelli, Martina Mealier, Anne-Laure Damianou, Andreas Metta, Giorgio Prescott, Tony J. Demiris, Yiannis Dominey, Peter Ford Verschure, Paul F.M.J. Laboratory for Synthetic Perceptive Emotive and Cognitive Systems Universitat Pompeu Fabra Barcelona08002 Spain Barcelona Spain Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London SW7 2AZ United Kingdom Robot Cognition Laboratory INSERM U846 Stem Cell and Brain Research Institute Bron69675 France Italian Institute of Technology iCub Facility Via Morego 30 Genova Italy Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science University of Sheffield United Kingdom

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users. Copyright © 2017, The Authors. All rights reserved.

关键词： Human robot interaction

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Towards multi-functional light-weight long-term real-time coastal ocean observation system 11

Towards multi-functional light-weight long-term real-time co...

引用

11th ACM International Conference on Underwater Networks and systems, WUWNet 2016

作者： Jiang, Zhenfeng Ma, Yanming Chen, Jiali Wang, Zigeng Peng, Zheng Liu, Jun Han, Guitao Smart Ocean Industry Research Institute Nantong Jiangsu226000 China Computer Science and Engineering Department University of Connecticut StorrsCT06269 United States Computer Science Deparment City College of New York NY10031 United States Computer Science and Technology Department Jilin University Changchun Jilin130012 China State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

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Believing in BERT: Using expressive communication to enhance trust and counteract operational error in physical Human-robot interaction

Believing in BERT: Using expressive communication to enhance...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Adriana Hamacher Nadia Bianchi-Berthouze Anthony G. Pipe Kerstin Eder University College UCL Interaction Centre London UK Bristol Robotics Laboratory Bristol UK Verification and Validation for Safety in Robots research theme Bristol Robotics Laboratory Bristol UK Department of Computer Science University of Bristol UK

Strategies are necessary to mitigate the impact of unexpected behavior in collaborative robotics, and research to develop solutions is lacking. Our aim here was to explore the benefits of an affective interaction, as opposed to a more efficient, less error prone but non-communicative one. The experiment took the form of an omelet-making task, with a wide range of participants interacting directly with BERT2, a humanoid robot assistant. Having significant implications for design, results suggest that efficiency is not the most important aspect of performance for users; a personable, expressive robot was found to be preferable over a more efficient one, despite a considerable trade off in time taken to perform the task. Our findings also suggest that a robot exhibiting human-like characteristics may make users reluctant to `hurt its feelings'; they may even lie in order to avoid this.

关键词： Bit error rate Collaboration Humanoid robots Reliability Electronic mail Speech recognition

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Optoelectronic Devices: Thin Metallic Heat Sink for Interfacial Thermal Management in Biointegrated Optoelectronic Devices (Adv. Mater. Technol. 11/2018)

引用

Advanced Materials Technologies 2018年第11期3卷

作者： Han Hee Jung Juwon Song Shuang Nie Han Na Jung Min Seok Kim Jae-Woong Jeong Young Min Song Jizhou Song Kyung-In Jang Department of Robotics Engineering Department of Information and Communication Engineering Brain Engineering Convergence Center Daegu Gyeongbuk Institute of Science and Technology Daegu 49288 Republic of Korea Department of Engineering Mechanics Soft Matter Research Center and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou 310027 China School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 123 Cheomdan-gwagiro Buk-gu Gwangju 61005 Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea

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Control laws development for a free-flying unmanned robotic system to support interplanetary bodies prospecting and characterization missions

Control laws development for a free-flying unmanned robotic ...

引用

AIAA Guidance, Navigation, and Control Conference, 2016

作者： Perez, Andres E. Moncayo, Hever Prazenica, Richard J. Zacny, Kris Mueller, Robert P. Ebert, Tom Dupuis, Michael Flight Dynamics and Control Research Laboratory Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Aerospace Engineering Department Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Exploration Technology Group Honneybee Robotics Company CA United States Exploration Research and Technology Programs Science and Technology Projects Division NASA Kennedy Space Center FL United States Autonomous Flight Systems Lead Testing and Design Branch NASA Kennedy Space Center FL United States Robotics and Autonomous Systems Testing and Design Branch NASA Kennedy Space Center FL United States

ISBN: (数字)9781624103896

ISBN: (纸本)9781624103896

In situ Resource Utilization (ISRU) facilitates planetary exploration by drawing needed resources, such as water, from the local environment. However, the extreme nature of these environments require the development of advanced unmanned space systems integrated with sample-capture devices to achieve the ultimate goal of prospecting these resources. This paper presents the design, development and Hardware-in-the-Loop (HIL) simulation testing of guidance and tracking control laws for an autonomous small marsupial free-flyer prospector system. The control laws are based on an extended non-linear dynamic inversion (NLDI) approach and its implementation is illustrated through HIL simulation using a mathematical model of an autonomous vehicle research platform developed by NASA Kennedy Space Center. This vehicle has been designed to support the development, testing and validation of algorithms for safe, reliable, and scalable control space missions with minimal need for human intervention in complex, unstructured environments. The main objective of the control laws is to minimize 3-axis distances with respect to a desired trajectory and maintain stability and adequate performance in the presence of uncertainties. The performance of the control laws is evaluated during autonomous flight in terms of trajectory tracking errors, real-time execution on board the flight computer, and control activity at nominal and dynamically-changing conditions. The results show that for all mission cases investigated the control laws approach has desirable capabilities and is reliable for in-flight testing operation as a next step towards the validation and verification of this configuration. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hardware-in-the-loop simulation

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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