检索结果-内蒙古大学图书馆

作者： Ma, Kede Li, Hui Yong, Hongwei Wang, Zhou Meng, Deyu Zhang, Lei Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computing. Hong Kong Polytechnic University Hong Kong Hong Kong Institute for Information and System Sciences and Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China Inst. for Info. and Syst. Sci. and Min. of Educ. Key Lab. of Intelligent Networks and Netwk. Secty. Xi'an Jiaotong University Xi'an710049 China

We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three components separately, we reconstruct a desired patch and place it back into the fused image. This novel patch decomposition approach benefits MEF in many aspects. First, as opposed to most pixel-wise MEF methods, the proposed algorithm does not require post-processing steps to improve visual quality or to reduce spatial artifacts. Second, it handles RGB color channels jointly, and thus produces fused images with more vivid color appearance. Third and most importantly, the direction of the signal structure component in the patch vector space provides ideal information for ghost removal. It allows us to reliably and efficiently reject inconsistent object motions with respect to a chosen reference image without performing computationally expensive motion estimation. We compare the proposed algorithm with 12 MEF methods on 21 static scenes and 12 deghosting schemes on 19 dynamic scenes (with camera and object motion). Extensive experimental results demonstrate that the proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes. Moreover, it maintains a lower computational cost compared with the state-of-The-Art deghosting schemes.11The MATLAB code of the proposed algorithm will be made available online. Preliminary results of Section III-A [1] were presented at the IEEE International Conference on Image Processing, Canada, 2015. © 1992-2012 IEEE.

关键词： Image fusion

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Computational challenges in modeling & simulation of complex systems 17

Computational challenges in modeling & simulation of complex...

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Simulation Winter Conference

作者： Richard M. Fujimoto Christopher Carothers Alois Ferscha David Jefferson Margaret Loper Madhav Marathe Simon J. E. Taylor School of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA Johannes Kepler Univeristy Linz Linz AUSTRIA Lawrence Livermore National Laboratory Livermore CA USA Information & Communications Laboratory Georgia Tech Research Institute Atlanta GA USA Network Dynamics and Simulation Science Virginia Tech Blacksburg VA USA Department of Computer Science Brunel University London Uxbridge UK

ISBN: (纸本)9781538634271

Modeling and simulation faces many new computational challenges in the design of complex engineered systems. The systems that need to be modeled are increasingly interconnected and interdependent, achieving unprecedented levels of complexity. The computational platforms upon which simulations execute have undergone dramatic changes in recent years. Position statements by leading researchers are presented concerning important computational challenges and opportunities facing the M&S community.

关键词： Handheld computers Conferences Computational modeling Complex systems Engines Buffer storage

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Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics

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Genome biology 2021年第1期22卷 246页

作者： Kim-Anh Lê Cao Al J Abadi Emily F Davis-Marcisak Lauren Hsu Arshi Arora Alexis Coullomb Atul Deshpande Yuzhou Feng Pratheepa Jeganathan Melanie Loth Chen Meng Wancen Mu Vera Pancaldi Kris Sankaran Dario Righelli Amrit Singh Joshua S Sodicoff Genevieve L Stein-O'Brien Ayshwarya Subramanian Joshua D Welch Yue You Ricard Argelaguet Vincent J Carey Ruben Dries Casey S Greene Susan Holmes Michael I Love Matthew E Ritchie Guo-Cheng Yuan Aedin C Culhane Elana Fertig Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. kimanh.lecao@nimelb.edu.au. Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. McKusick-Nathans Institute of the Department of Genetic Medicine Johns Hopkins School of Medicine Baltimore MD USA. Data Science Dana-Farber Cancer Institute Boston MA USA. Biostatistics Harvard TH Chan School of Public Health Boston MA USA. Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA. Centre de Recherches en Cancérologie de Toulouse (INSERM) Université Paul Sabatier III Toulouse France. Cancer Convergence Institute and Division of Quantitative Sciences Department of Oncology Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD USA. Department of Mathematics and Statistics McMaster University Hamilton Canada. Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS) School of Life Sciences Technical University of Munich Munich Germany. Department of Biostatistics UNC Chapel Hill NC USA. Barcelona Supercomputing Center Barcelona Spain. Department of Statistics University of Wisconsin Madison WI USA. Department of Statistical Sciences University of Padova Padova PD Italy. Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada. PROOF Centre of Excellence Vancouver BC Canada. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. Department of Neuroscience Johns Hopkins University Baltimore MD USA. Kavli Neuroscience Discovery Institute Johns Hopkins University Baltimore MD USA. Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering University of Michiga

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Development of a computer-aided design software for dental splint in orthognathic surgery

arXiv

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arXiv 2017年

作者： Chen, Xiaojun Li, Xing Xu, Lu Sun, Yi Politis, Constantinus Egger, Jan Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China OMFS IMPATH Research Group Department of Imaging and Pathology Faculty of Medicine KU Leuven Leuven Belgium Department of Oral and Maxillofacial Surgery University Hospitals Leuven Leuven Belgium Faculty of Computer Science and Biomedical Engineering Institute for Computer Graphics and Vision Graz University of Technology Graz Austria BioTechMed-Graz Graz Austria

In the orthognathic surgery, dental splints are important and necessary to help the surgeon reposition the maxilla or mandible. However, the traditional methods of manual design of dental splints are difficult and time-consuming. The research on computer-aided design software for dental splints is rarely reported. Our purpose is to develop a novel special software named EasySplint to design the dental splints conveniently and efficiently. The design can be divided into two steps, which are the generation of initial splint base and the Boolean operation between it and the maxilla-mandibular model. The initial splint base is formed by ruled surfaces reconstructed using the manually picked points. Then, a method to accomplish Boolean operation based on the distance filed of two meshes is proposed. The interference elimination can be conducted on the basis of marching cubes algorithm and Boolean operation. The accuracy of the dental splint can be guaranteed since the original mesh is utilized to form the result surface. Using EasySplint, the dental splints can be designed in about 10 minutes and saved as a stereo lithography (STL) file for 3D printing in clinical applications. Three phantom experiments were conducted and the efficiency of our method was demonstrated. Copyright © 2017, The Authors. All rights reserved.

关键词： computer software

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5G ENTERING INTO A NEW ERA

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China Communications 2017年第12期14卷 I0002-I0004页

作者： Feifei Gao Changle Li Jun Fang Kaizhi Huang Zhi Quan Chengwen Xing Jianhua Zhang Zhongshan Zhang Liqiang Zhao Yulong Zou Department of Automation Tsinqhua UniversityBeiiingChina the State Key Laboratory of Integrated Services Networks Xidian University china communications ieee communications magazine ieee signal processing letters national digital switching system engineering&technological research center the college of information engineering shenzhen university the school of information and electronics beijing institute of technology bupt itu-r imt-2020 channel model ieee comm the school of computer and communication engineering in the university of science and technology beijing(ustb) state key lab.of integrated ser vice networks xidian university the nanjing university of posts and telecommunications(nupt)

The ever increasing wireless data services, such asimaging, video, audio, multimedia, etc., have de-mands for the very high speed wireless communicationsand network, which are unfortunately a bottleneck whencombining with the wireline core network. Users＇ arenow expecting high quality of experience with low-costdevices, ubiquitous connectivity, energy efficiency, highreliability, or even ultra-low latency if a vehicle terminalis applied.

关键词： 5G ENTERING A NEW ERA

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Publisher Correction: Chalcogenide glass-on-graphene photonics

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Nature Photonics 2018年第3期12卷 185-185页

作者： Hongtao Lin Yi Song Yizhong Huang Derek Kita Skylar Deckoff-Jones Kaiqi Wang Lan Li Junying Li Hanyu Zheng Zhengqian Luo Haozhe Wang Spencer Novak Anupama Yadav Chung-Che Huang Ren-Jye Shiue Dirk Englund Tian Gu Daniel Hewak Kathleen Richardson Jing Kong Juejun Hu Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge USA Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology Cambridge USA Department of Electronic Engineering Xiamen University Xiamen China Key Laboratory of Optoelectronic Technology & System Education Ministry of China Chongqing University Chongqing China The College of Optics & Photonics University of Central Florida Orlando USA Optoelectronics Research Centre University of Southampton Southampton UK

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Fast High-resolution Miniature Two-photon Microscopy for Brain Imaging in Freely-behaving Mice at the Single-spine Level

Fast High-resolution Miniature Two-photon Microscopy for Bra...

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中国神经科学学会第十二届全国学术会议

作者： Weijian Zong Runlong Wu Mingli Li Yanhui Hu Yijun Li Jinghang Li Hao Rong Haitao Wu Yangyang Xu Yang Lu Hongbo Jia Ming Fan Zhuan Zhou Yunfeng Zhang Aimin Wang Liangyi Chen Heping Cheng State Key Laboratory of Membrane Biology Institute of Molecular Medicine PekingTsinghua Center for Life Sciences Peking University Department of Cognitive Sciences Institute of Basic Medical Sciences Department of Neurobiology Institute of Basic Medical Sciences Brain Research Instrument Innovation Center Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Engineering and Computer Science Peking University

Recent developments in miniaturized microscopes have furthered the quest to visualize brain activities and structural dynamics in animals engaged in self-determined behaviors. However, it remains an unmet challenge to resolve activity at single dendritic spines, the elemental units underlying neuronal computation, in freely-behaving animals. Here, we report the design, testing, and application of a fast, high-resolution, miniaturized two-photon microscope（FIRM-TPM） that accomplishes this goal. With a headpiece weighing 2.15 g and a new type of hollow-core photonic crystal fiber to deliver 920-nm femtosecond laser pulses, the FIRM-TPM is capable of imaging commonly used biosensors at high spatiotemporal resolution（0.64 μm laterally and 3.35 μm axially, 40 Hz at 256 × 256 pixels）. Its micro-electromechanical systems scanner also enables random-access capability and free-line scanning at up to 10,000 Hz. It compares favorably with benchtop two-photon microscopy and miniature wide-field fluorescence microscopy in the structural and functional imaging of Thy1-GFP-or GCa MP6 f-labeled neurons. Further, we demonstrate its unique application and robustness with hour-long recording of neuronal activities down to the level of spines in mice experiencing vigorous body and head movements or engaging in social interaction. Thus, our new generation miniature microscope provides neuroscientists the long-sought tool-of-choice for imaging the brain at the synaptic level in freely-behaving animals.

关键词：

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Joint power allocation and beamforming for non-orthogonal multiple access (NOMA) in 5G millimeter-wave communications

arXiv

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arXiv 2017年

作者： Xiao, Zhenyu Zhu, Lipeng Choi, Jinho Xia, Pengfei Xia, Xiang-Gen School of Electronic and Information Engineering Beihang University Beijing100191 China Beijing Key Laboratory for Network-Based Cooperative Air Traffic Management Beijing Laboratory for General Aviation Technology Beijing100191 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Korea Republic of School of Electronics and Information Engineering Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China College of Information Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

In this paper we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) communications (mmWave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mmWave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mmWave-NOMA to more-user mmWave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieve close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access (TDMA). Copyright © 2017, The Authors. All rights reserved.

关键词： Millimeter waves

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Measurement of the Drell–Yan forward-backward asymmetry and of the effective leptonic weak mixing angle in proton-proton collisions at s=13TeV

引用

Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2025年 866卷

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P.S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Waltenberger, W. Wulz, C.-E. Janssen, T. Van Laer, T. Van Mechelen, P. Breugelmans, N. Dansana, S. De Moor, A. Delcourt, M. Heyen, F. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G.P. Van Putte, S. Vannerom, D. Bilin, B. Clerbaux, B. Das, A.K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F.A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Shahzad, M.A. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, I.S. Giammanco, A. Guzel, A.O. Jain, Sh. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T.T. Wertz, S. Alves, G.A. Alves Gallo Pereira, M. Coelho, E. Correia Silva, G. Hensel, C. Menezes De Oliveira, T. Mora Herrera, C. Moraes, A. Rebello Teles, P. Soeiro, M. Vilela Pereira, A. Aldá Júnior, W.L. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Gomes De Souza, R. Laux Kuhn, T. Macedo, M. Martins, J. Mundim, L. Nogima, H. Pinheiro, J.P. Santoro, A. Sznajder, A. Thiel, M. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Maietto Silverio, I. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Laroze, D. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liang, Z. Liu, J. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Sh Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Guangdong Provincial Key Laboratory of Nuclear Science Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Nanjing Normal University Nanjing China Institute of Modern Physics Key Laboratory of Nuclear Physics Ion-beam Application (MOE) Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy

The forward-backward asymmetry in Drell–Yan production and the effective leptonic electroweak mixing angle are measured in proton-proton collisions at s=13 TeV, collected by the CMS experiment and corresponding to an integrated luminosity of 138fb⁻¹. The measurement uses both dimuon and dielectron events, and is performed as a function of the dilepton mass and rapidity. The unfolded angular coefficient A₄ is also extracted, as a function of the dilepton mass and rapidity. Using the CT18Z set of parton distribution functions, we obtain sin²⁡θ_eff^ℓ=0.23152±0.00031, where the uncertainty includes the experimental and theoretical contributions. The measured value agrees with the standard model fit result to global experimental data. This is the most precise sin²⁡θ_eff^ℓ measurement at a hadron collider, with a precision comparable to the results obtained at LEP and SLD. © 2025 CERN for the benefit of the CMS Collaboration

关键词： CMS Drell-Yan Electroweak Mixing angle

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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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