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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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An Improved Epsilon Constraint Handling Method Embedded in MOEA/D for Constrained Multi-objective Optimization Problems

An Improved Epsilon Constraint Handling Method Embedded in M...

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IEEE Symposium Series on Computational Intelligence

作者： Zhun Fan Wenji Li Xinye Cai Hui Li Han Huang Zhaoquan Cai Caimin Wei Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Department of Electronic Engineering Shantou University 515063 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi 710049 China Department of Mathematics Shantou University Guangdong 515063 China Department of Electronic Engineering Shantou University Guangdong 515063 China School of Software Engineering South China University of Technology Guangdong Guangzhou 510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu 210016 China Department of Computer Science Huizhou University Guangdong 516007 China

ISBN: (纸本)9781509042418

This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). More specifically, it dynamically adjusts the epsilon level, which is a critical parameter in the epsilon constraint method, according to the feasible ratio of solutions in the current population. In order to verify the effect of the improved epsilon constraint handling method, three algorithms - MOEA/D-CDP, MOEA/D-Epsilon, and MOEA/D-IEpsilon (MOEA/D with the improved epsilon constraint handling mechanism) are tested on nine CMOPs (CMOP1-CMOP9). The comprehensive experimental results indicate that the proposed epsilon constraint handling method is very effective on the performance of both convergence and diversity.

关键词： improved epsilon constraint

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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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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays

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Neural networks : the official journal of the International Neural Network Society 2016年 79卷 117-27页

作者： Peng Liu Zhigang Zeng Jun Wang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: liupengjz@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: hustzgzeng@***. Department of Computer Science College of Science and Engineering City University of Hong Kong Kowloon Tong Hong Kong. Electronic address: jwang.cs@cityu.edu.hk.

This paper addresses the multistability for a general class of recurrent neural networks with time-varying delays. Without assuming the linearity or monotonicity of the activation functions, several new sufficient conditions are obtained to ensure the existence of (2K+1)(n) equilibrium points and the exponential stability of (K+1)(n) equilibrium points among them for n-neuron neural networks, where K is a positive integer and determined by the type of activation functions and the parameters of neural network jointly. The obtained results generalize and improve the earlier publications. Furthermore, the attraction basins of these exponentially stable equilibrium points are estimated. It is revealed that the attraction basins of these exponentially stable equilibrium points can be larger than their originally partitioned subsets. Finally, three illustrative numerical examples show the effectiveness of theoretical results.

关键词： Cohen–Grossberg neural networks Multistability Non-monotonic activation functions Time-varying delays

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Circuit design and exponential stabilization of memristive neural networks

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Neural networks : the official journal of the International Neural Network Society 2015年 63卷 48-56页

作者： Shiping Wen Tingwen Huang Zhigang Zeng Yiran Chen Peng Li Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: wenshiping226@***. Texas A & M University at Qatar Doha 5825 Qatar. Electronic address: tingwen.huang@qatar.tamu.edu. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng527@***. Electrical and Computer Engineering Department University of Pittsburgh Pittsburgh PA 15261 USA. Electronic address: yic52@pitt.edu. Department of Electrical and Computer Engineering Texas A & M University College Station TX 77843 USA. Electronic address: pli@tamu.edu.

This paper addresses the problem of circuit design and global exponential stabilization of memristive neural networks with time-varying delays and general activation functions. Based on the Lyapunov-Krasovskii functional method and free weighting matrix technique, a delay-dependent criteria for the global exponential stability and stabilization of memristive neural networks are derived in form of linear matrix inequalities (LMIs). Two numerical examples are elaborated to illustrate the characteristics of the results. It is noteworthy that the traditional assumptions on the boundness of the derivative of the time-varying delays are removed.

关键词： Memristor Neural networks Stabilization

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2D-PCA shape models: Application to 3D reconstruction of the human teeth from a single image

2D-PCA shape models: Application to 3D reconstruction of the...

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3rd International MICCAI Workshop on Medical computer vision, MCV 2013

作者： Abdelrehim, Aly S. Farag, Aly A. Shalaby, Ahmed M. El-Melegy, Moumen T. Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 United States Electrical Engineering Department Assiut University Assiut 71516 Egypt

ISBN: (纸本)9783319055299

Accurate modeling of human teeth is mandatory for many reasons. Some of them are (1) Providing comfort to patients during mold process, and (2) Enhancing the accuracy level for oral orthodontist and dental care personnel. Shape from Shading (SFS) proved to be of great help in this problem, considering the fact that human tooth surface is textureless. In this paper, we address the problem of 3D tooth reconstruction, and improve the current algorithms. The approached developed in this paper reconstructs the teeth from single image shading with 2D-PCA shape priors that have more sophisticated reflectance model. Oren-Nayar-Wolff model was used for modeling the surface reflectance. This formulation uses shape priors as got from a set of training CT scans of real human teeth. Our experiments show promising quantitative results, which builds the infrastructure for having an optical based approach that accounts for inexpensive and radiationless human tooth reconstruction. © 2014 Springer International Publishing Switzerland.

关键词： computerized tomography

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Text-independent writer identification by feature fusion

Text-independent writer identification by feature fusion

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International Conference on Intelligent Control and Information processing (ICICIP)

作者： Yongjie Hu Wenming Yang Youbin Chen Department of Electronic Engineering of Tsinghua University Zhejiang University Research interests include digital image processing and computer vision Department of Electronic Engineering of Tsinghua University Research interests include digital image processing and pattern recognition

ISBN: (纸本)9781479936519

This paper explores text-independent writer identification by combining Bag of Features (BoF), contour-hinge and SIFT scales feature. The BoF method adopted differs from the common BoF approach for writer identification in that it extracts SIFT descriptors and uses Locality-constrained Linear Coding to get feature vector of each document. The Locality-constrained Linear Coding (LLC) tries to reconstruct each feature through locality constraint and has much more discriminative power than the common used Vector Quantization (VQ). Contour-hinge feature can capture orientation and curvature of the ink trace. Modification is made to the original contour-hinge to improve the identification rate. Besides, we also use SIFT scale information and integrate these three kinds of features together. Experiments are conducted the challenging ICDAR2013 writer identification contest dataset and dataset for "ICFHR2012 Writer Identification Contest, Challenge 1: Latin Documents". The experiment results show that the proposed BoF approach outperforms the common ones that adopt VQ, and after the integration, our method achieves the best result on the entire ICDAR2013 and ICFHR2012 dataset under soft evaluation.

关键词： Feature extraction Vectors Encoding image reconstruction Text analysis Educational institutions

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The Infrared Multispectral image Simulation Research Based on 3D Scene

The Infrared Multispectral Image Simulation Research Based o...

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2013 3rd International Conference on Electric and electronics(EEIC 2013)

作者： Bo Li Huai-ci Zhao Mei Du Jinfeng Lv Department of Optical-Electronics and Information Processing Shenyang Institute of automation Chinese Academy of Science University of Chinese Academy of Science Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision Shenyang Institute of Engineering

A method of generating infrared multispectral simulation images by 3D scene is presented. The infrared images at any observation angle of the scene and a data cube of simulation images of specific waveband could be generated by the method. Some experiments have been done in waveband 3～5μm to generate a data cube involving 401 infrared simulation images in the wavelength step 0.005μm, and the experiments results show that the simulation method presented in this paper could generate infrared multispectral images data cube rapidly. A way to reflect the differences while the observation angle changed is mentioned in this paper.

关键词： infrared multispectral simulation the data cube self-radiation

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NOVEL VARIATIONAL APPROACH FOR THE PERSPECTIVE SHAPE FROM SHADING PROBLEM USING CALIBRATED imageS

NOVEL VARIATIONAL APPROACH FOR THE PERSPECTIVE SHAPE FROM SH...

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IEEE International Conference on image processing

作者： Aly Abdelrahim Hossam Abdelmunim James Graham Aly Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville Louisville KY 40292 USA. Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo 11517 Egypt. ECE Dept. University of Louisville Louisville KY 40292 USA.

ISBN: (纸本)9781479923427

In this paper, We propose a new approach for solving the Lamber-tian shape from shading (SFS) problem. The proposed technique employs image information as well as camera calibration parameters. We claim that the incorporation of camera parameters results in a better surface reconstruction. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. We propose a new variational formulation that relates an evolving surface model with image information, taking into consideration that the image is taken by a perspective camera with known parameters. A new energy functional is formulated to incorporate brightness, smoothness, and integrability constraints. All of these terms assume a hyper surface that evolves in time to meet their criteria. Gradient descent optimization with Euler-Lagrange is used for optimization. We constructed a database of real surfaces as a ground-truth for the experiments. The proposed approach shows superior performance when compared with other approaches in the literature.

关键词： camera calibration perspective SFS variational approach camera calibration artificial shade images Perspective image information ground truth surface modeling Surface reconstruction

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