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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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Potential numerical techniques and challenges for atmospheric modeling

Potential numerical techniques and challenges for atmospheri...

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作者： Li, J. Steppeler, J. Fang, F. Pain, C.C. Zhu, J. Peng, X. Dong, L. Li, Y. Tao, L. Leng, W. Wang, Y. Zheng, J. International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Hamburg Germany Applied Modelling and Computation Group Department of Earth Science and Engineering Imperial College London London United Kingdom International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Department of Atmospheric Sciences University of Chinese Academy of Sciences Beijing China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China State Key Laboratory of Scientific and Engineering Computing Chinese Academy of Sciences Beijing China School of Mathematics and Statistics Henan University Kaifeng China Center for Excellence in Regional Atmospheric Environment Institute of Urban Environment Chinese Academy of Sciences Xiamen China

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Nonretarded edge plasmon-polaritons in anisotropic two-dimensional materials

arXiv

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arXiv 2019年

作者： Margetis, Dionisios Maier, Matthias Stauber, Tobias Low, Tony Luskin, Mitchell Institute for Physical Science and Technology Department of Mathematics Center for Scientific Computation and Mathematical Modeling University of Maryland College ParkMD20742 United States Department of Mathematics Texas A&M University College StationTX77843 United States Materials Science Factory Instituto de Ciencia de Materiales de Madrid CSIC MadridE-28049 Spain Department of Electrical & Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Mathematics University of Minnesota MinneapolisMN55455 United States

By an integral equation approach to the time-harmonic classical Maxwell equations, we describe the dispersion in the nonretarded frequency regime of the edge plasmon-polariton (EPP) on a semi-infinite flat sheet. The sheet has an arbitrary, physically admissible, tensor valued and spatially homogeneous conductivity, and serves as a model for a family of two-dimensional conducting materials. We formulate a system of integral equations for the electric field tangential to the sheet in a homogeneous and isotropic ambient medium. We show how this system is simplified via a length scale separation. This view entails the quasi-electrostatic approximation, by which the tangential electric field is replaced by the gradient of a scalar potential, . By the Wiener-Hopf method, we solve an integral equation for in some generality. The EPP dispersion relation comes from the elimination of a divergent limiting Fourier integral for at the edge. We connect the existence, or lack thereof, of the EPP dispersion relation to the index for Wiener-Hopf integral equations, an integer of topological character. We indicate that the values of this index may express an asymmetry due to the material anisotropy in the number of wave modes propagating on the sheet away from the edge with respect to the EPP direction of propagation. We discuss extensions such as the setting of two semi-infinite, coplanar sheets. Our theory forms a generalization of the treatment by Volkov and Mikhailov (1988 Sov. Phys. JETP 67 1639). Copyright © 2019, The Authors. All rights reserved.

关键词： Integral equations

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Design of microresonators to minimize thermal noise below the standard quantum limit

arXiv

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arXiv 2019年

作者： Sharifi, S. Banadaki, Y.M. Cullen, T. Veronis, G. Dowling, J.P. Corbitt, T. Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States School of Electrical Engineering & Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70808 United States Hearne Institute for Theoretical Physics Louisiana State University Baton RougeLA70803 United States Department of Computer Science Southern University Baton RougeLA70813 United States

We present a design for a new microresonator whose geometry is optimized to maximize subStandard Quantum Limit (SQL) performance. The new design is predicted to have thermal noise well below the SQL across a broad range of frequencies when operated at 10K. The performance of this designed microresonator will allow it to serve as a test-bed for quantum non-demolition measurements, and to open new regimes of precision measurement that are relevant for many practical sensing applications, including advanced gravitational wave detectors. Copyright © 2019, The Authors. All rights reserved.

关键词： Resonators

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Energy efficiency optimization for secure transmission in MISO cognitive radio network with energy harvesting

arXiv

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arXiv 2019年

作者： Zhang, Miao Cumanan, Kanapathippillai Thiyagalingam, Jeyarajan Wang, Wei Burr, Alister G. Ding, Zhiguo Dobre, Octavia A. School of Information Science and Technology Nantong University Nantong China Department of Electronic Engineering University of York York United Kingdom Scientific Computing Department of Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus Didcot United Kingdom Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Department of Electrical and Computer Engineering Memorial University St. JohnsNLA1B 3X5 Canada

In this paper, we investigate different secrecy energy efficiency (SEE) optimization problems in a multiple-input single-output underlay cognitive radio (CR) network in the presence of an energy harvesting receiver. In particular, these energy efficient designs are developed with different assumptions of channels state information (CSI) at the transmitter, namely perfect CSI, statistical CSI and imperfect CSI with bounded channel uncertainties. In particular, the overarching objective here is to design a beamforming technique maximizing the SEE while satisfying all relevant constraints linked to interference and harvested energy between transmitters and receivers. We show that the original problems are non-convex and their solutions are intractable. By using a number of techniques, such as non-linear fractional programming and difference of concave (DC) functions, we reformulate the original problems so as to render them tractable. We then combine these techniques with the Dinkelbach's algorithm to derive iterative algorithms to determine relevant beamforming vectors which lead to the SEE maximization. In doing this, we investigate the robust design with ellipsoidal bounded channel uncertainties, by mapping the original problem into a sequence of semidefinite programs by employing the semidefinite relaxation, non-linear fractional programming and S-procedure. Furthermore, we show that the maximum SEE can be achieved through a search algorithm in the single dimensional space. Numerical results, when compared with those obtained with existing techniques in the literature, show the effectiveness of the proposed designs for SEE maximization. Copyright © 2019, The Authors. All rights reserved.

关键词： Cognitive radio

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A meta-analysis of survival and prognostic factors in multiple system atrophy

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Journal of neurology 2025年第7期272卷 467页

作者： Xiao Dong Daji Chen Linlin Wan Linliu Peng Zhao Chen Riwei Ouyang Xiafei Long Kefang Du Xiaokang Wu Xinying Xiao Ruqing He Rong Qiu Beisha Tang Hong Jiang Department of Neurology Xiangya Hospital Central South University Changsha 410008 China. Key Laboratory of Hunan Province in Neurodegenerative Disorders Central South University Changsha 410008 China. National Clinical Research Center for Geriatric Disorders Xiangya Hospital Central South University Changsha 410008 China. Department of Radiology Xiangya Hospital Central South University Changsha 410008 China. National International Collaborative Research Center for Medical Metabolomics Central South University Changsha 410008 China. Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases Changsha 410008 China. School of Computer Science and Engineering Central South University Changsha 410083 China. Department of Neurology Xiangya Hospital Central South University Changsha 410008 China. jianghong73868@***. Department of Neurology The Third Xiangya Hospital of Central South University Changsha 410008 China. jianghong73868@***. Key Laboratory of Hunan Province in Neurodegenerative Disorders Central South University Changsha 410008 China. jianghong73868@***. National Clinical Research Center for Geriatric Disorders Xiangya Hospital Central South University Changsha 410008 China. jianghong73868@***. National International Collaborative Research Center for Medical Metabolomics Central South University Changsha 410008 China. jianghong73868@***. Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases Changsha 410008 China. jianghong73868@***.

BACKGROUND:Multiple-system atrophy is a rapidly progressive neurodegenerative disease with incomplete survival data, limiting the understanding of long-term outcomes. This study aimed to investigate a comprehensive data including survival time and prognostic factors. METHODS:Individual patient data were pooled from studies reporting Kaplan-Meier curves, and then, survival curves were generated. The pooled median survival times were derived using weighted median of medians approach and hazard ratios of risk factors were analyzed using either fixed- or random-effects model. RESULTS:37 studies involving 6145 patients were included. The median survival time for MSA patients was 8.23 years (95% CI 8.02-8.56) based on reconstructed individual patient data. The pooled weighted median time was 8.0 years (95% CI 7.51-9.0). The following variables were found as unfavorable prognostic factors (hazard ratio with 95% CI are shown): age at onset (1.02, 1.01-1.03), poor levodopa response (1.55, 1.14-2.11), parkinsonism onset (1.30, 1.05-1.62), falls (1.84, 1.4-2.4), dysautonomia onset (1.48, 1.16-1.9), autonomic failure (2.52, 1.42-4.48), orthostatism hypotension (1.39, 1.16-1.66), bladder catheterization (1.81, 1.41-2.31), and stridor (1.5, 1.12-2.02). CONCLUSION:Survival time in MSA was evaluated using multiple methodological approaches, revealing a median survival of 8.0 years, and clinical variables like early autonomic failure and frequently falls were identified as predictors of poor survival outcomes.

关键词： Meta-analysis Multiple-system atrophy Prognostic factors Survival time

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An Algorithm for Describing the Convex and Concave Shape of Protein Surface

An Algorithm for Describing the Convex and Concave Shape of ...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Wei Wang Keliang Li Hehe Lv Lin Sun Hongjun Zhang Jinling Shi Shiguang Zhang Yun Zhou Yuan Zhao Jingjing Xv Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining School of Aviation Engineering Anyang University School of International Education Xuchang University

Protein surface plays a key role in many biological *** proteins participate in the life activities of cells via binding to other proteins or ligand molecules. It is an important work to study protein structure and function by analyzing the protein surface shape. Based on the CX algorithm and the 2 D fngerprint-base method, we proposed a FCX method to identify the morphology of bulges and depressions on the protein surface. The experimental results show that the FCX algorithm has a more desirable outcome than CX algorithm. The FCX algorithm has a higher correlation with the convex and concave features than CX values with solvent accessibility, solvent accessibility, and Bfactor's Pearson correlation coefficient. This result shows that the FCX algorithm can describe the shape of the protein surface residues more accurately than the CX algorithm.

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An information-theoretic framework for non-linear canonical correlation analysis

arXiv

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arXiv 2018年

作者： Painsky, Amichai Feder, Meir Tishby, Naftali School of Electrical Engineering Tel Aviv University Ramat Aviv Tel Aviv6997801 Israel School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Givat Ram Jerusalem9190401 Israel

Canonical Correlation Analysis (CCA) is a linear representation learning method that seeks maximally correlated variables in multi-view data. Non-linear CCA extends this notion to a broader family of transformations, which are more powerful for many real-world applications. Given the joint probability, the Alternating Conditional Expectation (ACE) provides an optimal solution to the non-linear CCA problem. However, it suffers from limited performance and an increasing computational burden when only a finite number of observations is available. In this work we introduce an information-theoretic framework for the non-linear CCA problem (ITCCA), which extends the classical ACE approach. Our suggested framework seeks compressed representations of the data that allow a maximal level of correlation. This way we control the trade-off between the flexibility and the complexity of the representation. Our approach demonstrates favorable performance at a reduced computational burden, compared to non-linear alternatives, in a finite sample size regime. Further, ITCCA provides theoretical bounds and optimality conditions, as we establish fundamental connections to rate-distortion theory, the information bottleneck and remote source coding. In addition, it implies a "soft" dimensionality reduction, as the compression level is measured (and governed) by the mutual information between the original noisy data and the signals that we extract. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal distortion

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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ParSECH: Parallel sequencing error correction with hadoop for large-scale genome sequences 9

ParSECH: Parallel sequencing error correction with hadoop fo...

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9th International Conference on Bioinformatics and computational Biology, BICOB 2017

作者： Das, Arghya Kusum Shams, Shayan Goswami, Sayan Platania, Richard Lee, Kisung Park, Seung-Jong School of Electrical Engineering and Computer Science Center for Computation and Technology Louisiana State University Baton RougeLA70808 United States

ISBN: (纸本)9781943436071

A scalable and accurate error correction tool is essential for all next-generation sequencing (NGS) projects as high-throughput sequencing machines have started producing terabytes of data with significantly higher error-rates compared to conventional Sanger sequencing. In this paper, we develop ParSECH, a scalable and fully distributed error correction software based on kappa;-mer spectrum analysis, without the need of a reference genome. To achieve high scalability over terabytes of data and hundreds of cores, ParSECH utilizes two opensource big data frameworks: Hadoop and Hazelcast. To achieve high accuracy, unlike existing error correction tools that use a single kappa;-mer coverage cutoff to detect errors, ParSECH determines the skewness involved in the kappa;-mer coverage of each individual read, followed by correcting the errors in each read separately for low and high coverage regions of the genome. We demonstrate the scalability of ParSECH by correcting the errors of both simulated and real whole human genome data with coverage ranging from 2x to 40x. ParSECH can correct the largest dataset (452GB human genome), which could not be handled by the existing error correction tools, in about 39 hours. For a small *** genome dataset, ParSECH demonstrates 94% accuracy, higher than 90% accuracy of Quake.

关键词： Error correction

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