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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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Comparison of two robust predictive control strategies for trajectory tracking of flexible-joint robots

Comparison of two robust predictive control strategies for t...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： M. Makarov M. Grossard P. Rodriguez-Ayerbe D. Dumur SUPELEC Systems Sciences (E3S) Automatic Control Department France CEA Interactive Robotics Laboratory France

ISBN: (纸本)9781479957378

Two control design approaches are proposed for robust and accurate trajectory tracking of flexible-joint robot manipulators using motor-side measurements only. Within a two-degrees-of-freedom linear controller structure with reference anticipation, the design methods are based either on the Generalized Predictive control (GPC) or the H ∞ frameworks. In the first case, the approach considers the Youla parametrization of an initial GPC controller, while in the second case, a two-degree-of-freedom H ∞ controller with anticipated reference is synthesized. Experimental evaluation of both methods is performed using the CEA lightweight ASSIST robot arm under rigid model-based compensation, which is modeled as an uncertain system and preliminary identified experimentally. Benefits and drawbacks of both methods are discussed.

关键词： Robustness Robots Joints Polynomials Uncertainty Frequency control Frequency-domain analysis

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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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Corrigendum to a Low-Cost, Light-Weight Climbing Robot for Inspection of Glass Curtains

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International Journal of Advanced Robotic systems 2015年第3期12卷 20-20页

作者： Ran Liang Meteb Altaf Eball Ahmad Rong Liu Ke Wang Robotics Institute School of Mechanical Engineering and Automation Beihang University Beijing China National Robotics & Intelligent Systems center King AbdulAziz City for Science and Technology Saudi Arabia Riyadh Saudi Arabia Arts et Métiers ParisTech Design Manufacturing and Control Laboratory (LCFC) Paris France

Corrigendum, corrigendum

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Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot

Implementation and test of human-operated and human-like ada...

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15th Annual Conference on Advances in Autonomous robotics systems, TAROS 2014

作者： Liang, Peidong Yang, Chenguang Wang, Ning Li, Zhijun Li, Ruifeng Burdet, Etienne Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University United Kingdom MOE Key Lab. of Autonomous System and Network Control College of Automation Science and Engineering South China University of Technology China State Key Laboratory of Robotics and System Harbin Institute of Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Bioengineering Imperial College of Science Technology and Medicine London United Kingdom

ISBN: (纸本)9783319104003

This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremental stiffness extraction method is developed, which uses instantaneous amplitude identified from EMG in a high frequency band, compensating for non-linear residual error in the linear mapping and preventing muscle fatigue from affecting the control. Experiments on one joint of the Baxter robot are carried out to test the approach in a disturbance attenuation task, and to compare it with automatic human-like impedance adaptation. The experimental results demonstrate that the new human operated impedance method is successful at attenuating disturbance, and results similarly to as automatic disturbance attenuation, thus demonstrating its efficiency. © 2014 Springer International Publishing.

关键词： Stiffness

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Slip prevention of a humanoid robot by coordinating acceleration vector

Slip prevention of a humanoid robot by coordinating accelera...

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International Conference on Information and Automation (ICIA)

作者： Zhangguo Yu Jing Li Qiang Huang Xuechao Chen Gan Ma Libo Meng Si Zhang Yan Liu Wen Zhang Weimin Zhang Xiaopeng Chen Junyao Gao Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China Intelligent Robotics Institute Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

A humanoid robot may suffer from slip since its two feet are not fixed on the ground. Slip occurrence may induce the loss of robot's balance. The previous literature focused on the detection or estimation of the slip occurrence, or only employed the translational acceleration regulation to overcome slip. This study proposes a slip prevention method by coordinating acceleration vector including the horizontal, vertical, and rotational acceleration components. The contribution of this paper is to regulate the translational and rotational acceleration together to prevent the slip occurrence. The effectiveness of our proposed method was verified by simulations and experiments on an actual humanoid robot.

关键词： Acceleration Robot kinematics Vectors Humanoid robots Mathematical model Equations

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A largely model independent solution for the position control of a typical inverted pendulum system

A largely model independent solution for the position contro...

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Chinese control Conference (CCC)

作者： Qiang Gao Shasha Chen Yi Li Junfang Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems School of Electrical Engineering Tianjin University of Technology Tianjin CN Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin Tianjin CN

An Active Disturbance Rejection based solution is proposed for the position control of a standard inverted pendulum system, demonstrating a largely model independent design that is distinctly different from existing model-based designs from classical as well as modern control theory. The quality of the control loop is further improved with a unique double disturbance observer design, which rejects independently the disturbance and uncertainty in both the pendulum itself and the cart that carries it. This proposed approach offers a promising and practical solution because it's intuitive to understand and easy to tune, in addition to its strong performance and effectiveness shown in both simulation study and real time experimentation.

关键词： Real-time systems Mathematical model Equations control systems Estimation Educational institutions Simulation

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Optimal Sensor Trajectories for Mobile Underwater Target Positioning with Noisy Range Measurements

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IFAC Proceedings Volumes 2014年第3期47卷 5139-5144页

作者： D. Moreno-Salinas A.M. Pascoal J. Aranda Department of Computer Science and Automatic Control National University Distance Education (UNED) Spain Laboratory of Robotics and Systems in Engineering and Science (LARSyS) Instituto Superior Tecnico (IST) University of Lisbon Portugal Adjunct Scientist National Institute of Oceanography (NIO) Goa India

There is considerable interest in reducing the number of sensors/beacons involved in underwater positioning/navigation systems since this has the potential to drastically reduce the costs and the time spent in deploying, calibrating, and recovering acoustic equipment at sea. Motivated by these considerations, we address the problem of single underwater target positioning based on acoustic range measurements between the target and a moving sensor at the sea surface. In particular, the goal of the present work is to compute optimal geometric trajectories for the surface sensor that will, in a well defined sense, maximize the range-related information available for underwater target positioning and tracking. To this effect, the Fisher Information Matrix and the maximization of its determinant are used to determine the sensor trajectory that yields the most accurate positioning of the target, while the latter describes a preplanned trajectory. It is shown that the optimal trajectory depends on the velocity of the sensor, the velocity and trajectory of the target, the sampling time between measurements, the measurement error model, and the number of measurements used to compute the FIM. Simulation examples illustrate the key results derived.

关键词： Autonomous underwater vehicles Fisher information matrix Cramer-Rao Bound optimization positioning single tracker

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Bipedal walking with toe-off, heel-strike and compliance with external disturbances

Bipedal walking with toe-off, heel-strike and compliance wit...

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IEEE-RAS International Conference on Humanoid Robots

作者： Xuechao Chen Yuhang Zhou Qiang Huang Zhangguo Yu Gan Ma Libo Meng Chenglong Fu Key Laboratory of Intelligent Control and Decision of Complex System China Ministry of Education Key Laboratory of Biomimetic Robots and Systems China Intelligent Robotics Institute Beijing Institute of Technology Beijing China Department of Mechanical Engineering Tsinghua University Beijing

ISBN: (纸本)9781479971756

Both disturbance rejection and human-like motions, like toe-off and heel-strike, are important for a biped robot to enhance its performance. However, the required motions for them may influence each other, which is why few studies consider them simultaneously. This paper presents a method to realize stable walking with toe-off and heel-strike even when the robot experiences disturbances. We propose a walking controller which can constrain the desired ground reaction force. On the one hand, the controller can adjust the torso acceleration to make the supporting leg compliant with the external disturbances on the torso. On the other hand, it can rotate the supporting foot by adjusting the ZMP to an appropriate location. The linear inverted pendulum is utilized to generate the CoM trajectory and the foot placement. Meanwhile, its ZMP can be predefined as required so that the toe-off and heel-strike can be achieved by the controller. The effectiveness of the proposed method is demonstrated by simulations.

关键词： Legged locomotion Foot Torso Robot kinematics Acceleration Joints

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Dual adaptive control of bimanual manipulation with online fuzzy parameter tuning

Dual adaptive control of bimanual manipulation with online f...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Alex Smith Chenguang Yang Hongbin Ma Phil Culverhouse Angelo Cangelosi Etienne Burdet Center for Robotics and Neural Systems Plymouth University Devon UK Key Laboratory of Autonomous System and Network Control South China University of Technology Guangzhou P. R. China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China Department of Bioengineering Imperial College London UK

A biomimetic controller with online adaptation of impedance and force is applied to a full kinematic and dynamic model of the Baxter bimanual robot. A set of fuzzy logic engines are proposed to infer the values of tuning gains which affect the control performance and control effort of the controller, which would conventionally be set to a static value based on expert knowledge of the controller; the aim of this being to avoid the use of arbitary values to set these values. A simulated experiment is carried out, where the Baxter robot is required to move an object through a trajectory while subjected to two different disturbance forces in four phases. The controller with fuzzy inferred control gains is compared against the same controller with fixed gains to gauge the effectiveness of the new method. Results show that fuzzy inference of control gains impart an improvement in both tracking error and control effort.

关键词： Trajectory Fuzzy logic Force Dynamics Mathematical model Manipulators

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