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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

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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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Optimal search strategies for pollutant source localization

Optimal search strategies for pollutant source localization

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Behzad Bayat Naveena Crasta Howard Li Auke Ijspeert BioRobotics Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland Institute for Systems and Robotics (ISR) Instituto Superior Tecnico (IST) University of Lisbon Portugal COBRA Department of Electrical and Computer Engineering University of New Brunswick Fredericton E3B 5A3 Canada

ISBN: (纸本)9781509037636

This paper is aimed at developing optimal motion planning for a single autonomous surface vehicle (ASV) equipped with an on-board pollutant sensor that will maximize the sensor-related information available for source seeking. The ASV uses a nonlinear diffusion model of the pollutant source to estimate the intensity/level of the pollution at the present ASV location. The rate of detection of particles depends on the relative distance between the ASV and the source. First, we use a probabilistic map of the source location built through the sensor information for a dynamic motion planning of source seeking based on an entropy reduction formulation, where an appropriately defined Fisher information matrix (FIM) is used for entropy reduction or information gain. We derive the FIM for the set-up and investigate optimal trajectories. Next, we present an online nonlinear Monte Carlo algorithm that uses the obtained sensor information about pollutant at different vehicle locations to update a probabilistic uncertainty map of pollutant source location. As the mission unfolds the ASV motion is computed by considering a moving-horizon interval of decision, which will allow for the inclusion of new information available for optimal motion planning. The proposed motion planning approach is extended to take into account external disturbances and it is able to minimize the uncertainty in the pollutant source. Finally, we provide two case studies to demonstrate efficacy of the proposed motion planning algorithm.

关键词： Position measurement Pollution measurement Uncertainty Planning Probabilistic logic Robot sensing systems Pollution

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Polar-Grid Representation and Kriging-Based 2.5D Interpolation for Urban Environment Modelling

Polar-Grid Representation and Kriging-Based 2.5D Interpolati...

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International Conference on Intelligent Transportation

作者： Cristiano Premebida Joao Sousa Luis Garrote Urbano Nunes Department of Electrical and Computer Engineering (DEEC) Institute of Systems and Robotics (ISR) University of Coimbra Portugal

In this paper a spatial interpolation approach, based on polar-grid representation and Kriging predictor, is proposed for 3D point cloud sampling. Discrete grid representation is a widely used technique because of its simplicity and capacity of providing an efficient and compact representation, allowing subsequent applications such as artificial perception and autonomous navigation. Two-dimensional occupancy grid representations have been studied extensively in the past two decades, and recently 2.5D and 3D grid-based approaches dominate current applications. A key challenge in perception systems for vehicular applications is to balance low computational complexity and reliable data interpretation. To this end, this paper contributes with a discrete 2.5D polar-grid that upsamples the input data, ie sparse 3D point cloud, by means of a deformable kriging-based interpolation strategy. Experiments carried out on the KITTI dataset, using data from a LIDAR, demonstrate that the approach proposed in this work allows a proper representation of urban environments.

关键词： Three-dimensional displays Interpolation Laser radar Computational modeling Robots Solid modeling Estimation

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Energy Savings Potential Associated with Stator Winding Connection Mode Change in Induction Motors

Energy Savings Potential Associated with Stator Winding Conn...

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International Conference on electrical Machines

作者： Fernando J. T. E. Ferreira Anibal T. de Almeida Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra 3030-290 Coimbra Portugal

ISBN: (纸本)9781509025398

In industry, a large share of the direct-on-line three-phase squirrel-cage induction motors is driving variable-load applications, such as conveyors, mixers, presses, saws, escalators, etc. In these cases, the motor efficiency and power factor can be significantly improved by means of automatically changing the stator winding connection mode to star during light load periods and to delta during heavy load periods. In this paper, the energy savings potential associated with this strategy is analyzed in detail for motors of different efficiency classes and the results of two in-field tests of a new electronic device for automatic load-based connection mode management in variable-load, fixed-speed motors, are presented. The in-field experimental results confirm the estimated energy savings, demonstrating the energy benefits of the automatic star-delta connection management in induction motors driving variable-load applications.

关键词： Direct-on-line motors Energy savings Induction motor Stator winding Connection mode Motor efficiency Motor power factor Variable-load motors

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Facial expression recognition based on EOG toward emotion detection for human-robot interaction 8

Facial expression recognition based on EOG toward emotion de...

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8th International Conference on Bio-Inspired systems and Signal Processing, BIOSIGNALS 2015

作者： Cruz, Aniana Garcia, Diogo Pires, Gabriel Nunes, Urbano Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Department of Engineering Polytechnic Institute of Tomar Tomar Portugal

ISBN: (纸本)9789897580697

The ability of an intelligent system to recognize the user's emotional and mental states is of considerable interest for human-robot interaction and human-machine interfaces. This paper describes an automatic recognizer of the facial expression around the eyes and forehead based on electrooculographic (EOG) signals. Six movements of the eyes, namely, up, down, right, left, blink and frown, are detected and reproduced in an avatar, aiming to analyze how they can contribute for the characterization of facial expression. The recognition algorithm extracts time and frequency domain features from EOG, which are then classified in real-time by a multiclass LDA classifier. The offline and online classification results showed a sensitivity around 92% and 85%, respectively.

关键词： Human robot interaction

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Remote lab for Stochastic Computing using reconfigurable logic

Remote lab for Stochastic Computing using reconfigurable log...

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Experiment@ International Conference (exp.at)

作者： José Domingos Alves Jorge Lobo DEEC - Department of Electrical and Computer Engineering FCT University of Coimbra ISR - Institute of Systems and Robotics - University of Coimbra

In this interactive demonstration we show the basics of Stochastic Computing (SC) using a remote reconfigurable logic laboratory. A very simple web based approach is used to enable a remote access to a development board hosting a FPGA (Field Programmable Gate Array). The board switches and keys are virtualised with an online web form, and a webcam is used to provide feedback to the user. Recently, SC has been revisited and evaluated as a possible way of performing approximate probabilistic computations for artificial perception systems. Therefore, we provide the basic stochastic computing modules, so that any user can use them to build a stochastic computing circuit and go beyond software simulations, providing a remote hardware device to test real circuits at high clock speeds.

关键词： Hardware Field programmable gate arrays Generators computers Computational modeling Integrated circuit modeling Remote laboratories

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Rewinding Strategy Aided by FEA as a Solution to Increase Efficiency of Industrial Motors

Rewinding Strategy Aided by FEA as a Solution to Increase Ef...

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International Conference on electrical Machines

作者： V. P. B. Aguiar R. S. T. Pontes T. R. Fernandes Neto F. J. T. E. Ferreira Motor Driven Systems Laboratory Department of Electrical Engineering Federal University of Ceará Fortaleza CE 60440554 Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9781509025398

This paper presents a proper model and methodology to improve the efficiency class of a squirrel-cage induction motor during rewinding. The modeling approach is based on the finite element model, i.e. two-dimensional model coupled with the rotor equivalent circuit (first stage) and the stator equivalent circuit (second stage). Results obtained with finite element analysis are compared with those obtained with analytical calculations. It is demonstrated that, during rewinding, if an improved winding configuration is used, efficiency gains can be obtained, allowing to improve the efficiency class of the motor, from IE2 to IE3, respecting the specified limits of NEMA & IEC performance indicators, namely, breakdown torque, locked-rotor torque, and locked-rotor current. In 79% of the simulated cases the efficiency gain allowed the studied motor to reach the IE3 class.

关键词： Finite element analysis Equivalent circuits IEEE standards IEC standards ANSI standards Stator Induction motors Electromagnetic modeling Frequency-domain analysis

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An analysis of sampling effect on bilateral teleoperation system transparency 34

An analysis of sampling effect on bilateral teleoperation sy...

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34th Chinese Control Conference, CCC 2015

作者： Ting, Yang Fu, Yi Li Tavakoi, Mahdi State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin Heilongjiang China Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada

ISBN: (纸本)9789881563897

The performances of continuous-time controlled and discrete-time controlled bilateral teleoperation systems are mathematically and experimentally studied and compared. The results show that under stability conditions, continuous-time controlled teleoperation systems have hybrid matrix parameters that are closer to their ideal values than their discrete-time controlled counterparts. This means better force tracking and position tracking performance under continuous-time control, which can lead to better task performance. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Bilateral teleoperation continuous-time control discrete-time control hybrid parameters system performance

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A Computationally Efficient Implementation of Fictitious Play in a Distributed Setting

A Computationally Efficient Implementation of Fictitious Pla...

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European Signal Processing Conference

作者： BRIAN SWENSON SOUMMYA KAR JOAO XAVIER Department of Electrical and Computer Engineering Carnegie Mellon University Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Tecnico University of Lisbon

ISBN: (纸本)9781479988518

The paper deals with distributed learning of Nash equilibria in games with a large number of players. The classical fictitious play (FP) algorithm is impractical in large games due to demanding communication requirements and high computational complexity. A variant of FP is presented that aims to mitigate both issues. Complexity is mitigated by use of a computationally efficient Monte-Carlo based best response rule. Demanding communication problems are mitigated by implementing the algorithm in a network-based distributed setting, in which player-to-player communication is restricted to local subsets of neighboring players as determined by a (possibly sparse, but connected) preassigned communication graph. Results are demonstrated via a simulation example.

关键词： Games Distributed Learning Fictitious Play Nash Equilibrium

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