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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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Improvement of Back-EMF waveform by adjusting pole angle in surface-mounted permanent magnet synchronous machinetype Generatorfor Fly-Wheel 28

Improvement of Back-EMF waveform by adjusting pole angle in ...

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28th International Electric vehicle Exhibition, EVS 2015

作者： Shin, Young-Jin Gi, Joon-Woo Yoon, Myung-Hwan Chai, Seung-Hee Hong, Jung-Pyo Department of Automotive Electronic Control Engineering Hanyang University Seoul Korea Republic of Department of Advanced Power Conversion System Engineering Hanyang University Seoul Korea Republic of Department of Automotive Engineering Hanyang University Seoul Korea Republic of

ISBN: (纸本)9781510809260

This paper proposes a method to improve the waveform of the back-EMF to obtain sinusoidal waveform. The improvement of back-EMF waveform has many advantages such as reduction of torque ripple, noise and vibration. A dent is discovered from the middle of the back-EMF waveform of the Surface-Mounted Permanent Magnet Synchronous Machine (SPM) type vehicle-generator. This phenomenon occurred due to the flux leakage and causes higher total harmonic distortion (THD). The flux leakage variation occurs due to the relative position between permanent magnet and teeth. A method of widening intervals of the permanent magnets is suggested for the solution of this problem. The flux leakage can be constant as permanent magnet interval is wider than slot opening. This solution is applied to the target generator and THD reduction of back-EMF is confirmed. Not only THD reduced from 19.5% to 5.8% but also the torque ripple reduced by more than 30%. The proposed method is verified by the results of finite element analysis.

关键词： Permanent magnets

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Early-stage resource evaluation method for ECU integration of hybrid electric vehicles 28

Early-stage resource evaluation method for ECU integration o...

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28th International Electric vehicle Exhibition, EVS 2015

作者： Kang, Eunhwan Park, I. Chung, J. Sunwoo, M. Lee, W. Youn, J. Yim, S. Won, D. Department of Automotive Engineering Graduate School Hanyang University Seoul Korea Republic of Hanyang University Seoul Korea Republic of Department of Automotive Engineering Hanyang University Seoul Korea Republic of Department of Control and Instrumentation Engineering Changwon National University Changwon-si Kyungsangnam-do Korea Republic of Hyundai-Kia R and D Center Eco-Vehicle Control System Development Team Hwaseong-si Gyeonggi-do Korea Republic of

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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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A Least-Squares Regression Based Method for vehicle Yaw Moment of Inertia Estimation

A Least-Squares Regression Based Method for Vehicle Yaw Mome...

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American control Conference

作者： Zitian Yu Xiaoyu Huang Junmin Wang Vehicle System and Control Laboratory Mechanical and Aerospace Engineering Department The Ohio State University Columbus OH 43210 USA

ISBN: (纸本)9781479917730

vehicle yaw moment of inertia is an important parameter for many vehicle dynamic models and control systems yet it is usually difficult to estimate. A methodology in estimating the vehicle yaw moment of inertia is presented in this article by studying the linear relationship between vehicle lateral acceleration, yaw acceleration, and rear wheel lateral tire forces. This linear relationship is derived by manipulating the equations in the single-track model such that the front wheel force disappears in the equation. Based on the linear relationship, the common global positioning system (GPS) measurement error--antenna bias angle, can be tuned based on the symmetric assumption of vehicle left and right dynamics and a least-squares regression (LSR). A lag-like lateral tire force model is applied to capture the transient dynamics of the lateral tire force. The parameter determining relaxation length of the tire model can also be tuned based on a similar LSR method. Finally, the yaw moment of inertia can be estimated from an LSR again after knowing the estimated rear wheel lateral force. Simulation results in CarSim show that this proposed method is capable of generating reasonable estimations of yaw moment of inertia without knowing the front road wheel steering angle.

关键词： vehicle yaw moment of inertia transient lateral force relaxation length estimation least-squares vehicle dynamics relaxation length yawing moments Inertial forces vehicle dynamics rear wheel vehicle Tire forces linear relation MOMENT OF INERTIA

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Cover Feature: Ultrasensitive, Sustainable, and Selective Electrochemical Hydrazine Detection by ALD-Developed Two-Dimensional WO3(ChemElectroChem 2/2018)

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ChemElectroChem 2017年第2期5卷

作者： Zihan Wei Zhenyin Hai Mohammad Karbalaei Akbari Prof. Jie Hu Lachlan Hyde Prof. Stephen Depuydt Prof. Francis Verpoort Prof. Serge Zhuiykov Ghent University Global Campus Department of Applied Analytical & Physical Chemistry 119 Songdomunhwa-ro Yeonsu-gu Incheon South Korea Micro and Nano System Research Center Key Lab of Advanced Transducers and Intelligent Control System (Ministry of Education) & College of Information Engineering Taiyuan University of Technology Taiyuan 030024 Shanxi PR China Factory of the Future Swinburne University of Technology Hawthorn Australia National Research Tomsk Polytechnic University Lenin Avenue 30 634050 Tomsk Russian Federation Laboratory of Organometallics Catalysis and Ordered Materials State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Chemical and Material Engineering Wuhan University of Technology Wuhan P.R. China

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Model-free observer for MIMO systems

Model-free observer for MIMO systems

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IEEE Conference on control and Applications, CCA 2015

作者： Younes, Younes Al Noura, Hassan Rabhi, Abdelhamid Hajjaji, Ahmed El Mechanical Engineering Faculty Higher Colleges of Technology Al Ain United Arab Emirates University of Picardie Jules Verne Amiens France Electrical Engineering Department United Arab Emirates University Al Ain United Arab Emirates Modeling Information and System Lab Control and Vehicle Group University of Picardie Amiens France

ISBN: (纸本)9781479977871

In this paper, a novel observer design is introduced to estimate the outputs of a Multi-Input-Multi-Output (MIMO) system. From the synthesis of state observer and Model-Free technique, the Model-Free Observer (MFO) is proposed to compensate for the un-modeled dynamics, modeling errors, and the system uncertainties. The developed output estimation technique using MFO is validated using real flights of a quadrotor UAV. Two flight-test missions are conducted to validate the proposed observer performance, in the fault free case as well as in the presence of actuator faults. © 2015 IEEE.

关键词： MIMO systems

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A study of the red-tide monitoring system using Drifting Buoy and Wireless Networks 25

A study of the red-tide monitoring system using Drifting Buo...

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25th International Ocean and Polar engineering Conference, ISOPE 2015

作者： Shibata, Hiroshi Miyake, Hiroaki Tokimune, Hiromasa Hamasaki, Atsushi Matsuyama, Yukihiko Department of Electronic Control Engineering Hiroshima College National Institute of Technology Oosakikamijima Hiroshima Japan Advanced Course of Industry System Engineering Hiroshima College National Institute of Technology Oosakikamijima Hiroshima Japan Fisheries Research Agency Seikai National Fisheries Research Institute Nagasaki Nagasaki Japan

ISBN: (纸本)9781880653890

Red tides off the Japanese coast caused serious damages to aquaculture industries in every year (Honjo 1994). To reduce fisheries damage due to red tide, it is important to inform aquaculturist of red tides in short-term scale distribution. We developed a monitoring system which collects real-time data with a buoy to detect the occurrence of red tides on the sea. Our system can obtain data from a buoy drifted on the sea. The buoy has a GPS and two kinds of sensors that measure temperature and illuminance, and can connect to the Internet using mobile phone network. This paper describes a composition of our system, and a visualization of these data. In our experiments, the observing data was collected by the buoy in the middle of the Yatsushiro Sea. According to the experimental results, it was found that the movement of the buoy was mainly driven by tide current, rather than wind. Copyright © 2015 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Monitoring

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Synchronous Consensus of Double-Integrator Continuous-Time Multi-Agent systems with Switching Topologies and Time-Varying Delays

Synchronous Consensus of Double-Integrator Continuous-Time M...

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IEEE Annual Conference on Decision and control

作者： Hajar Atrianfar Mohammad Haeri Electrical Engineering Department Amirkabir University of Technology Tehran Iran Advanced Control System Lab Electrical Engineering Department Sharif University of Technology Tehran Iran

ISBN: (纸本)9781479978878

In this paper, we propose a class of second-order continuous-time consensus protocols in networks with bounded communication delay and switching interaction topologies. We present a sufficient condition under which the information states of all agents approach common values. The convergence analysis relies on properties ofnonnegative matrices and graph theory and requires that the union of communication graphs has a spanning tree over a bounded period of time. In addition, convergence in leader-following consensus is proved for switching topology and time-varying agents' awareness. Also the appropriate feedback gains are computed via given mathematical relations.

关键词： Multi-agent systems Time-varying delays Double-integrator continuous-time systems Synchronous consensus Switching topologies Leader-following

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A Study on Precipitation Kinetics of an Al-Cu-Li Alloy

A Study on Precipitation Kinetics of an Al-Cu-Li Alloy

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2015 3rd International Conference on Recent Trends in Materials and Mechanical engineering (ICRTMME 2015)

作者： Hyun Ho Jung Ye Rim Lee Kyung Ju Min Ho-Sung Lee Department of Space Launch Vehicle System Engineering University of Science & Technology Advanced Materials and Structure Team Korea Aerospace Research Institute 45 Eoun-dong

Aluminum-Copper-Lithium alloys are of strategic important[1] in lightweight aerospace structures due to their high specific modulus and specific strength and are under consideration for one of the potential choices for Korea Space Launch vehicle structure. However, this is one of the most complicated alloys due to the precipitation of a large number of second phase particles. In this study, the precipitation kinetics of an Al-4.3%Cu-1.2%Li alloy was studied by differential scanning calorimetry(DSC) technique since precipitation plays an important role for hardening of this alloy. Several models including Kissinger's, Starink's and Chaturvedi's were used to obtain the activation energy and precipitation kinetics.

关键词： Al-Cu-Li alloy Precipitation Activation Energy Differential scanning calorimetry

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