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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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International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW) Vila real, Portugal. 01-03 December 2016 Abstracts

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BMC SPORTS science MEDICINE AND REHABILITATION 2017年第SUPPL 1期9卷 5-5页

作者： [Anonymous] GAIA Association of Electronic and Information Technologies in the Basque Country Bilbao Spain Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal INESC TEC and Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal Vienna University of Technology Vienna Austria motion3D GmbH Vienna Austria Department of Psychology Masaryk University Faculty of Arts Brno Czech Republic Universidade Nova de Lisboa NOVA IMS Lisboa Portugal NOVA IMS Lisboa Portugal Department of Sport Sciences and CIDESD University of Beira Interior Covilhã Portugal Department of Sport Sciences University of Beira Interior and Research Centre in Sports Sciences Health Sciences and Human Development Covilhã Portugal Department of Sport Sciences University of Beira Interior Research Centre in Sports Sciences Health Sciences and Human Development Covilhã Portugal INESC TEC and University of Trás-os-Montes and Alto Douro Vila Real Portugal Digital Sports Group Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nuremberg (FAU) Erlangen Germany Institute for Factory Automation and Production Systems Friedrich-Alexander University Erlangen-Nuremberg (FAU) Erlangen Germany Fisio Lógic Centro de Fisioterapia Lisboa Portugal Faculdade de Motricidade Humana Universidade de Lisboa Laboratório de Comportamento Motor Estrada da Costa 1499-002 Cruz Quebrada Portugal Universidade de Trás-os-Montes e Alto Douro (UTAD) Vila Real Portugal Universidade de Trás-os-Montes e Alto Douro (UTAD) and Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência (INESC TEC) Vila Real Portugal INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal

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Common spatial pattern with feature scaling (FSc-CSP) for motor imagery classification 17th

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17th International Conference on Asia Simulation, AsiaSim 2017

作者： Prathama, Yohanes de Britto Hertyasta Shapiai, Mohd Ibrahim Aris, Siti Armiza Mohd Ibrahim, Zuwairie Jaafar, Jafreezal Fauzi, Hilman Department of Electronic Systems Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala LumpurWP Kuala Lumpur54100 Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala LumpurWP Kuala Lumpur54100 Malaysia Razak School of Engineering and Advanced Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala LumpurWP Kuala Lumpur54100 Malaysia Faculty of Electrical and Electronic Engineering Universiti Malaysia Pahang PekanPahang26600 Malaysia Department of Computer and Information Sciences Faculty of Science and Information Technology Universiti Teknologi Petronas Seri Iskandar Malaysia Faculty of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9789811064623

Brain-computer Interface (BCI) is a way to translate human thoughts into computer commands. One of the most popular BCI type is Electroencephalography (EEG)-based BCI, where motor imagery is considered one of the most effective ways. Previously, to extract useful information, various filters are introduced, such as spatial, temporal, and spectral filtering. A spatial filtering algorithm called Common Spatial Pattern (CSP) was developed and known to have excellent performance, especially in motor imagery for BCI application. In general, there are several approaches in improving CSP such as regularization approach, analytic approach, and frequency band selection. In general, the existing techniques for band selection is either to select or reject the band by ignoring the importance of the band. For example, Binary Particle Search Optimization Common Spatial Pattern (BPSO-CSP) was proposed to choose multiple possible best bands to be used in processing the data. In this paper, we propose an algorithm called Feature Scaling Common Spatial Pattern (FSc-CSP) to overcome the problem of feature selection. Instead of selecting features, the proposed algorithm employs a feature scaling system to scale the importance of each band by using Genetic Algorithm (GA) altogether with Extreme Learning Machine (ELM) as classifier, with 1 signifying the most important bands, declining until 0 for the unused bands, as opposed to the 1 and 0 selection system used in BPSO-CSP. Conducted experiments show that by employing feature scaling, better results can be achieved especially compared to vanilla CSP and feature selection with 100 hidden nodes in three from five BCI Competition III datasets IVa, namely aa, aw and ay, with around 5–8% better results compared to vanilla CSP and feature selection. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Brain computer interface

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Fingerprints selection for topological localization

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AIP Conference Proceedings 2017年第1期1863卷

作者： Vladimir Popov Ural Federal University Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg Russian Federation

Problems of visual navigation are extensively studied in contemporary robotics. In particular, we can mention different problems of visual landmarks selection, the problem of selection of a minimal set of visual landmarks, selection of partially distinguishable guards, the problem of placement of visual landmarks. In this paper, we consider one-dimensional color panoramas. Such panoramas can be used for creating fingerprints. Fingerprints give us unique identifiers for visually distinct locations by recovering statistically significant features. Fingerprints can be used as visual landmarks for the solution of various problems of mobile robot navigation. In this paper, we consider a method for automatic generation of fingerprints. In particular, we consider the bounded Post correspondence problem and applications of the problem to consensus fingerprints and topological localization. We propose an efficient approach to solve the bounded Post correspondence problem. In particular, we use an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms. In robotic experiments, we consider the average accuracy of reaching of the target point for different lengths of routes and types of fingerprints.

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Task sequencing for autonomous robotic vacuum cleaners

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AIP Conference Proceedings 2017年第1期1863卷

作者： Anna Gorbenko Vladimir Popov Ural Federal University Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg Russian Federation

Various planning problems for robotic systems are of considerable interest. One of such problems is the problem of task sequencing. In this paper, we consider the problem of task sequencing for autonomous vacuum floor cleaning robots. We consider a graph model for the problem. We propose an efficient approach to solve the problem. In particular, we use an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms.

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An efficient algorithm for integrated task sequencing and path planning for robotic remote laser welding

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AIP Conference Proceedings 2017年第1期1863卷

Different planning problems for robotic remote laser welding are of considerable interest. In this paper, we consider the problem of integrated task sequencing and path planning for robotic remote laser welding. We propose an efficient approach to solve the problem. In particular, we consider an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms.

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Stereo parallel tracking and mapping for robot localization

Stereo parallel tracking and mapping for robot localization

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IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2015

作者： Pire, Taihu Fischer, Thomas Civera, Javier De Cristoforis, Pablo Berlles, Julio Jacobo Computer Science Department University of Buenos Aires Argentina Robotics Perception and Real-Time Group University of Zaragoza Spain

ISBN: (纸本)9781479999941

This paper describes a visual SLAM system based on stereo cameras and focused on real-time localization for mobile robots. To achieve this, it heavily exploits the parallel nature of the SLAM problem, separating the time-constrained pose estimation from less pressing matters such as map building and refinement tasks. On the other hand, the stereo setting allows to reconstruct a metric 3D map for each frame of stereo images, improving the accuracy of the mapping process with respect to monocular SLAM and avoiding the well-known bootstrapping problem. Also, the real scale of the environment is an essential feature for robots which have to interact with their surrounding workspace. A series of experiments, on-line on a robot as well as off-line with public datasets, are performed to validate the accuracy and real-time performance of the developed method. © 2015 IEEE.

关键词： Mapping

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Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

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International Journal of Automation and computing 2015年第3期12卷 243-253页

作者： David H.Owens Chris T.Freeman Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield School of Electronics and Computer Science University of Southampton Advanced Robotics Division Italian Institute of Technology

Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.

关键词： Iterative learning control learning control optimization linear systems robotics.

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Layered ensemble model for short-term traffic flow forecasting with outlier detection

Layered ensemble model for short-term traffic flow forecasti...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Amr Abdullatif Stefano Rovetta Francesco Masulli Department of Computer Science Robotics and Systems Engineering University of Genova Genova Italy College of Science and Technology Temple University Sbarro Institute for Cancer Research and Molecular Medicine Philadelphia PA USA

ISBN: (纸本)9781509011322

real time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taking into consideration both spatial (road links) and temporal (lag or past traffic flow values) information. We propose a Layered Ensemble Model (LEM) which combines Artificial Neural Networks and Graded Possibilistic Clustering obtaining an accurate forecast of the traffic flow rates with outlier detection. Experimentation has been carried out on two different data sets. The former was obtained from real UK motorway and the later was obtained from simulated traffic flow on a street network in Genoa (Italy). The proposed LEM model for short-term traffic forecasting provides promising results and given the ability for outlier detection, accuracy, robustness of the proposed approach, it can be fruitful integrated in traffic flow management systems.

关键词： Forecasting Predictive models Neural networks Roads Computational modeling Data models Industries

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

arXiv

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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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