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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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Acoustic emission and angular movement variations from early adulthood healthy knees to late adulthood osteoarthritic knees

Acoustic emission and angular movement variations from early...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Lik-Kwan Shark Hongzhi Chen Wei Quan John Goodacre the Applied Digital Signal and Image Processing Research Centre (ADSIP) University of Central Lancashire Preston UK the Applied Digital Signal and Image Processing Research Centre (ADSIP) University of Central Lancashire China Iron and Steel Research Institute Group Beijing China North West Coast Innovation Agency Lancaster Health Hub Lancaster University Lancaster UK

From sit-stand-sit movements performed by healthy and osteoarthritic knees in three different age bands, this paper presents the investigation of the relationship between joint angular movement measured using electro-goniometers and acoustic emission measured using piezoelectric sensors. By partitioning joint movement in each sit-stand-sit cycle into sub-movement phases based on ascending and descending as well as acceleration and deceleration, the statistical analysis reveals aged and osteoarthritic knees exhibiting increased asymmetry and more variable angular movement compared with young and age-matched healthy knees, particularly in the descending-deceleration phase. With further evidence of a clear difference in the number of acoustic emission events detected from different knee groups, the study suggests the descending-deceleration phase likely to be most informative for quantitative assessment of knee aging and condition.

关键词： Angular velocity Aging Knee Acoustic emission Sensors Correlation coefficient Acoustic measurements

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097 Identifying novel acoustic emission biomarkers for use in knee osteoarthritis clinical trials

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Rheumatology 2018年第SUPPL_3期57卷

作者： Goodacre, John Schlueter, Daniela K Shark, Lik-Kwan Spain, Lucy Platt, Nicola Mercer, Joe Waterton, John C Bowes, Mike Dixon, Mandy Huddleston, Jane Lancaster Health Hub Lancaster University Lancaster UNITED KINGDOM CHICAS Lancaster Medical School Lancaster University Lancaster UNITED KINGDOM Applied Digital Signal and Image Processing Research Centre University of Central Lancashire Preston UNITED KINGDOM North West School of Radiology The Christie Hospital NHS Trust Manchester UNITED KINGDOM Centre for Imaging Sciences University of Manchester Manchester UNITED KINGDOM Imorphics Ltd Stryker Corp Manchester UNITED KINGDOM

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A primal-dual method for optimal control and trajectory generation in high-dimensional systems

arXiv

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arXiv 2017年

作者： Kirchner, Matthew R. Hewer, Gary Darbon, Jérôme Osher, Stanley Image and Signal Processing Branch Research Directorate Code 4F0000D Naval Air Warfare Center Weapons Division China LakeCA93555 United States Electrical and Computer Engineering Department University of California Santa BarbaraCA93106 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Department of Mathematics University of California Los AngelesCA90095 United States

Presented is a method for efficient computation of the Hamilton-Jacobi (HJ) equation for time-optimal control problems using the generalized Hopf formula. Typically, numerical methods to solve the HJ equation rely on a discrete grid of the solution space and exhibit exponential scaling with dimension. The generalized Hopf formula avoids the use of grids and numerical gradients by formulating an unconstrained convex optimization problem. The solution at each point is completely independent, and allows a massively parallel implementation if solutions at multiple points are desired. This work presents a primal-dual method for efficient numeric solution and presents how the resulting optimal trajectory can be generated directly from the solution of the Hopf formula, without further optimization. Examples presented have execution times on the order of milliseconds and experiments show computation scales approximately polynomial in dimension with very small high-order coefficients. Copyright © 2017, The Authors. All rights reserved.

关键词： Numerical methods

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Time-optimal collaborative guidance using the generalized Hopf formula

arXiv

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arXiv 2017年

作者： Kirchner, Matthew R. Mar, Robert Hewer, Gary Darbon, Jérôme Osher, Stanley Chow, Y.T. Image and Signal Processing Branch Research Directorate Code 4F0000D Naval Air Warfare Center Weapons Division China LakeCA93555 United States Guidance Navigation and Control Branch Weapons and Energetics Department Code 472100D Naval Air Warfare Center Weapons Division China LakeCA93555 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Department of Mathematics University of California Los AngelesCA90095 United States

Presented is a new method for calculating the time-optimal guidance control for a multiple vehicle pursuit-evasion system. A joint differential game of k pursuing vehicles relative to the evader is constructed, and a Hamilton–Jacobi–Isaacs (HJI) equation that describes the evolution of the value function is formulated. The value function is built such that the terminal cost is the squared distance from the boundary of the terminal surface. Additionally, all vehicles are assumed to have bounded controls. Typically, a joint state space constructed in this way would have too large a dimension to be solved with existing grid-based approaches. The value function is computed efficiently in high-dimensional space, without a discrete grid, using the generalized Hopf formula. The optimal time-to-reach is iteratively solved, and the optimal control is inferred from the gradient of the value function. Copyright © 2017, The Authors. All rights reserved.

关键词： Vehicles

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Corrigendum to "syndetically proximal Pairs" [J. Math. Anal. Appl. 379 (2011) 656-663]

arXiv

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arXiv 2017年

作者： Li, Jian Moothathu, T.K. Subrahmonian Oprocha, Piotr Department of Mathematics Shantou University Shantou Guangdong515063 China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques Shantou University Shantou Guangdong515063 China School of Mathematics and Statistics University of Hyderabad Hyderabad500 046 India AGH University of Science and Technology Faculty of Applied Mathematics AL. Mickiewicza 30 KrakÃ³ W30-059 Poland National Supercomputing Centre IT4 Innovations Division of the University of Ostrava Institute for Research and Applications of Fuzzy Modeling 30. Dubna 22 Ostrava70103 Czech Republic

We give a counterexample to Theorem 9 in "Syndetically proximal pairs"[J. Math. Anal. Appl. 379 (2011) 656-663]. We also provide sufficient conditions for the conclusion of Theorem 9 to hold. Copyright ©... 详细信息

关键词： Optimization

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Acoustic emission sonification and magnetic resonance imaging-based kinematics for exploratory analysis of knee joints

Acoustic emission sonification and magnetic resonance imagin...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lik-Kwan Shark Kai Huang Wei Quan John C. Waterton Michael A. Bowes John Goodacre Applied Digital Signal & Image Processing Research Centre University of Central Lancashire Preston UK Imorphics Ltd Manchester Science Park Manchester UK Biomedical Imaging Institute University of Manchester Manchester UK Lancaster Health Hub Lancaster University Lancaster UK

ISBN: (纸本)9781509016129

By combining two different measurement modalities, namely, acoustic emission (AE) and magnetic resonance imaging (MRI), this paper presents a novel auditory-visual software environment for exploratory analysis of knee joints. Using this tool, it becomes possible to listen to AE that is sonified to indicate surface friction among internal joint components during movement, examine patient-specific knee kinematics constructed from MRI to visualize surface contact areas between internal joint components during movement, and conduct integrated analysis of a knee joint by interactive and synchronized playback of sonified AE and animated joint movement, as well as perform comparative analysis of two different knee joints by using stereo sound and simultaneous display.

关键词： Magnetic analysis Magnetic resonance Bandwidth Kinematics Friction image edge detection Biomedical imaging

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Improving the detection of mtbi via complexity analysis in resting - state magnetoencephalography

Improving the detection of mtbi via complexity analysis in r...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Marios Antonakakis Stavros I. Dimitriadis Andrew C. Papanicolaou George Zouridakis Michalis Zervakis Digital Image and Signal Processing Laboratory School of Electronic and Computer Engineering Technical University of Crete Chania Greece Neuroinformatics Group Aristotle University Thessaloniki Greece Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Cardiff United Kingdom Departments of Pediatrics and Anatomy and Neurobiology University of Tennessee Health Science Center Memphis TN USA Neuroscience Institute Le Bonheur Children's Hospital Memphis TN USA Departments of Engineering Technology Computer Science Biomedical Engineering and Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (纸本)9781509018185

Diagnosis of mild Traumatic Brain Injury (mTBI) is difficult due to the variability of obvious brain lesions using imaging scans. A promising tool for exploring potential biomarkers for mTBI is magnetoencephalography which has the advantage of high spatial and temporal resolution. By adopting proper analytic tools from the field of symbolic dynamics like Lempel-Ziv complexity, we can objectively characterize neural network alterations compared to healthy control by enumerating the different patterns of a symbolic sequence. This procedure oversimplifies the rich information of brain activity captured via MEG. For that reason, we adopted neural-gas algorithm which can transform a time series into more than two symbols by learning brain dynamics with a small reconstructed error. The proposed analysis was applied to recordings of 30 mTBI patients and 50 normal controls in δ frequency band. Our results demonstrated that mTBI patients could be separated from normal controls with more than 97% classification accuracy based on high complexity regions corresponding to right frontal areas. In addition, a reverse relation between complexity and transition rate was demonstrated for both groups. These findings indicate that symbolic complexity could have a significant predictive value in the development of reliable biomarkers to help with the early detection of mTBI.

关键词： Complexity theory Time series analysis Sensitivity Integrated circuits Indexes Brain Laplace equations

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