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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 and Stable Gait Planning of a Quadruped Robot for Trotting Over Uneven Terrains

Optimal and Stable Gait Planning of a Quadruped Robot for Tr...

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RSI/ISM International Conference on robotics and Mechatronics

作者： Mahdi Khorram S. Ali A. Moosavian Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems Lab Dept of Mech Eng K. N. Toosi Univ of Tech Tehran Iran Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems Lab Dept of Mech Eng K. N. Toosi Univ of Tech Tehran Iran

ISBN: (纸本)9781467372350

To obtain satisfactory performance for legged robots, they should be able to move on natural environment stably. In this article, the path planning of a quadruped robot in the motion over uneven terrains will be investigated. One of major problems for traversing uneven terrains is the robot stability. Thus, a condition to guarantee the robot stability over such terrains will be proposed. This condition defines a point with zero moment in 3D space. This condition makes it possible to vary of the height of main body stably during trotting which is a main issue for traversing uneven terrains. Based on this condition, a linear ZMP equation will be defined so that the main body acceleration acts as control input and the ZMP acts as output. A linear quadratic regulator with stability constraint and fixed final states will be proposed to obtain the COG path which results in minimum acceleration. The tip of swing foot path planning will be fulfilled so that the generated path leads to avoid any collision with environment. Furthermore, the path of tip of swing legs is designed with minimum acceleration. The simulation results confirm the validity of proposed methods in terms of stability and the improvement of the consumption energy over uneven terrains.

关键词： Quadruped robots Dynamic stability criterion trot gait LQR control uneven terrains

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Multi-Criteria Design of 6-DoF Fully-Constrained Cable Driven Redundant Parallel Manipulator

Multi-Criteria Design of 6-DoF Fully-Constrained Cable Drive...

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RSI/ISM International Conference on robotics and Mechatronics

作者： Ali Nasr S. Ali A. Moosavian Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab. Department of Mechanical Engineering K.N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781467372350

The purpose of this paper is to optimize dimensions of KNTU 6-DoF cable driven redundant parallel manipulators. This manipulator is under investigation for possible high-speed application such as object manipulator. Usage of cable parallel manipulator depends on kinematic characteristic and control algorithm. This optimization is based on different performance indices including the singularity measure, the kinematic sensitivity, the stiffness and free collision workspace. Stiffness of the cable is also taken into consideration because of its effect on kinematic stiffness of moving platform manipulators performance. Points with cable collision, singular or actuator wrench infeasible configuration characteristic are eliminating from entire workspace.

关键词： Cable Robot Redundant Parallel Manipulator Kinematics Analysis Optimal Design

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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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State prediction approach for master-slave agents in presence of communication delay

State prediction approach for master-slave agents in presenc...

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2013 1st RSI/ISM International Conference on robotics and Mechatronics, ICRoM 2013

作者： Ghahremani, Sona Nili Ahmadabadi, Majid Mirian, Maryam S. Araabi, Babak N. Cognitive Robotics Lab. Dept. of Elect. and Comp. Eng. University of Tehran Iran Control and Intelligent Processing Center of Excellence Dept. of Elect. and Comp. Eng. University of Tehran Iran Robotics and Machine Intelligence Dept. of Elect. and Comp. Eng. University of Tehran Iran

ISBN: (纸本)9781467358118

Teleoperation is a mechanism that makes it possible to accomplish a task in an environment where the presence of the master at the slave site is either undesirable or hardly possible The communication delay is a challenge with undesirable effects, jeopardizing the teleoperated systems the most. When the salve agent is performing in a dynamically changing environment, communication delay can be very harmful since the environment is changing continuously even though the agent has stopped operating because of the delay. Many studies have been conducted to overcome time delay effects. © 2013 IEEE.

关键词： Remote control

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ESICM LIVES 2016: part one Milan, Italy. 1-5 October 2016 Abstracts

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INTENSIVE CARE MEDICINE EXPERIMENTAL 2016年第SUPPL 1期4卷 1-1页

作者： [Anonymous] Academic Medical Center University of Amsterdam Amsterdam Netherlands UMCU Utrecht Netherlands Autonomous University of Barcelona Barcelona Spain Hospital São Francisco Xavier Lisbon Portugal CHU Henri Mondor Medical ICU Créteil France Université de Paris Est-Créteil Groupe de Recherche Clinique CARMAS Créteil France Hôpital Tenon APHP Medical and Surgical ICU Paris France Université Paris Diderot Sorbonne Paris Cité UMR 1153 Paris France Interdepartmental Division of Critical Care University of Toronto Toronto Canada University Health Network University of Toronto Critical Care Medicine Toronto Canada University of Milan - Bicocca Health Sciences School of Medicine and Surgery Monza Italy St Michael’s Hospital University of Toronto Anesthesia Toronto Canada CHU d’Angers Medical ICU Angers France Interdepartmental Division of Critical Care University of Toronto Saint Michael’s Hospital and Keenan Research Centre Toronto Canada University of Birmingham Institute for Inflammation and Ageing Birmingham UK University of Sheffield Mathematics and Statistics Help Centre Sheffield UK Heart of England NHS Foundation Trust Intensive Care Medicine Birmingham UK Department of Surgery University Hospitals Birmingham NHS Trust Birmingham UK University Hospitals Birmingham NHS Trust Anaesthesia and Critical Care Medicine Birmingham UK Queen’s University of Belfast Wellcome-Wolfson Institute for Experimental Medicine Belfast UK Royal Victoria Hospital Intensive Care Medicine Belfast UK University of Warwick Warwick Clinical Trials Unit Coventry UK University of Bari ‘Aldo Moro’ Bari Italy Hospital Israelita Albert Einstein São Paulo Brazil Mayo Clinic Jacksonville USA Mahidol University Faculty of Tropical Medicine Bangkok Thailand University Medical Center Utrecht Intensive Care Utrecht Netherlands University Medical Center Utrecht Julius Center for Health Sciences and Primary Care Utrecht Netherlands Academic Medical Center University of Amste

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Conceptual imitation learning in a human-robot interaction paradigm

Conceptual imitation learning in a human-robot interaction p...

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作者： Hajimirsadeghi, Hossein Ahmadabadi, Majid Nili Araabi, Babak Nadjar Moradi, Hadi Cognitive Robotics Lab Control and Intelligent Processing Center of Excellence University of Tehran Iran

In general, imitation is imprecisely used to address different levels of social learning from high-level knowledge transfer to low-level regeneration of motor commands. However, true imitation is based on abstraction and conceptualization. This article presents a model for conceptual imitation through interaction with the teacher to abstract spatio-temporal demonstrations based on their functional meaning. Abstraction, concept acquisition, and self-organization of proto-symbols are performed through an incremental and gradual learning algorithm. In this algorithm, Hidden Markov Models (HMMs) are used to abstract perceptually similar demonstrations. However, abstract (relational) concepts emerge as a collection of HMMs irregularly scattered in the perceptual space but showing the same functionality. Performance of the proposed algorithm is evaluated in two experimental scenarios. The first one is a human-robot interaction task of imitating signs produced by hand movements. The second one is a simulated interactive task of imitating whole body motion patterns of a humanoid model. Experimental results show efficiency of our model for concept extraction, proto-symbol emergence, motion pattern recognition, prediction, and generation. © 2012 ACM.

关键词： Hidden Markov models

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Effect of flexible spine on stability of a passive quadruped robot: Experimental results

Effect of flexible spine on stability of a passive quadruped...

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2011 IEEE International Conference on robotics and Biomimetics, ROBIO 2011

作者： Kani, Mohammad Hasan H. Derafshian, Mostafa Bidgoly, Hamed Jalaly Ahmadabadi, Majid Nili Cognitive Robotics Lab. Control and Intelligent Processing Center of Excellence University of Tehran Iran

ISBN: (纸本)9781457721373

In rapid quadruped animals, the flexibility of spine is obviously observable while running. In this paper, we introduce a 3D passive dynamic quadruped robot, Fanari, that is capable to gallop down a slope without any external energy. We study the effect of spine flexibility on improvement of stability, adaptability and achievement of rapid leg locomotion. So a flexible spine that is capable to bend both outward and downward, a flexible spine that is only capable to bend outward and a rigid spine are designed and tested on the robot. Based on the case studies, we discuss about the challenges and the roles of spine stiffness on the gallop action. A simulation model is also made in order to corroborate the experimental results of the robot. It is concluded that the robot with the spine that is flexible in both outward and downward directions, has the most stability, adaptability and fastest velocity in its gallop action. © 2011 IEEE.

关键词： Multipurpose robots

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Effect of Flexible Spine on Stability of a Passive Quadruped Robot: Experimental Results

Effect of Flexible Spine on Stability of a Passive Quadruped...

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IEEE International Conference on robotics and Biomimetics

作者： Mohammad Hasan H. Kani Mostafa Derafshian Hamed Jalaly Bidgoly Majid Nili Ahmadabadi Cognitive Robotics Lab. Control and Intelligent Processing Center of Excellence School of ECE Faculty of Eng. University of Tehran

ISBN: (纸本)9781457721366

关键词： spines Spine quadruped robot Robot FLEXIBLE

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Seabed Tracking of an Autonomous Underwater Vehicle with Nonlinear Output Regulation

Seabed Tracking of an Autonomous Underwater Vehicle with Non...

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IEEE Conference on Decision and control

作者： A. Adhami-Mirhosseini A. Pedro Aguiar M. J. Yazdanpanah Control and Intelligent Processing Center of Excellence University of Tehran P.O. Box 14395/515 Tehran Iran Lab. of Robotics and Systems in Engineering and Science (LARSyS) Inst. Superior Tecnico (IST) Technical University of Lisbon Av. Rovisco Pais 1049-001 Lisboa Portugal

ISBN: (纸本)9781612848006

This paper proposes a new control algorithm for automatic seabed tracking performed by a low cost autonomous underwater vehicle subject to the fact that the seabed profile is not known in advance. The control design amounts to cast the problem in the framework of nonlinear output regulation theory combined with pseudo-spectral methods to approximate the solutions of the regulator equations. To this end, the seabed tracking problem is re-formulated as a trajectory tracking problem and the reference signal is constructed using Fourier series to approximate the seabed profiles. An interesting feature of this approach is that the combination of the Fourier series with output regulation problem allows to bypass the need to compute explicitly the Fourier coefficients. Stability analysis and simulation results with real seabed data show the effectiveness of the proposed controller.

关键词： Equations Regulators Mathematical model Vehicles Chebyshev approximation Vehicle dynamics

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