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arXiv 2020年

作者： Zhuang, Xiahai Xu, Jiahang Luo, Xinzhe Chen, Chen Ouyang, Cheng Rueckert, Daniel Campello, Victor M. Lekadir, Karim Vesal, Sulaiman RaviKumar, Nishant Liu, Yashu Luo, Gongning Chen, Jingkun Li, Hongwei Ly, Buntheng Sermesant, Maxime Roth, Holger Zhu, Wentao Wang, Jiexiang Ding, Xinghao Wang, Xinyue Yang, Sen Li, Lei School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Biomedical Image Analysis Group Imperial College London London United Kingdom Department Mathematics & Computer Science Universitat de Barcelona Barcelona Spain Friedrich-Alexander-Universität Erlangen-Nürnberg Germany School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Informatics Technical University of Munich Germany INRIA Université Côte d’Azur Sophia Antipolis France NVIDIA Bethesda United States School of Informatics Xiamen University Xiamen China College of Electrical Engineering Sichuan University Chengdu China Tencent AI Lab Shenzhen China

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, automated segmentation of LGE CMR is still challenging, due to the indistinguishable boundaries, heterogeneous intensity distribution and complex enhancement patterns of pathological myocardium from LGE CMR. Furthermore, compared with the other sequences LGE CMR images with gold standard labels are particularly limited, which represents another obstacle for developing novel algorithms for automatic segmentation of LGE CMR. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation and compare them objectively. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and unsupervised methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challeng

关键词： Magnetic resonance

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Search for Coordinates of WSN Nodes using Multidimensional Scaling Method

Search for Coordinates of WSN Nodes using Multidimensional S...

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International Conference on Ultra Modern Telecommunications and Workshops (ICUMT)

作者： Ilya Grishin Van Dai Pham Darina Okuneva Ruslan Kirichek Dept. of Communication Networks and Data Transmission Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia Dept. of Software Engineering and Computer Science Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781728192819

ISBN: (纸本)9781728192826

Nowadays, wireless sensor networks (WSN) are widely used for various Internet of Things applications. The sensor nodes are usually deployed randomly in the field. Thus, searching for node coordinates is one of the requirements to construct the network structure. With knowing distances between nodes and the coordinates of anchor nodes in a certain network fragment, we propose a method based on the multidimensional scaling for searching for desired node coordinates.

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Author Correction: Segment Anything for Microscopy

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Nature methods 2025年 2025 Jun 10页

作者： Anwai Archit Luca Freckmann Sushmita Nair Nabeel Khalid Paul Hilt Vikas Rajashekar Marei Freitag Carolin Teuber Melanie Spitzner Constanza Tapia Contreras Genevieve Buckley Sebastian von Haaren Sagnik Gupta Marian Grade Matthias Wirth Günter Schneider Andreas Dengel Sheraz Ahmed Constantin Pape Georg-August-University Göttingen Institute of Computer Science Goettingen Germany. German Research Center for Artificial Intelligence Kaiserslautern Germany. RPTU Kaiserslautern-Landau Kaiserslautern Germany. Department of General Visceral and Pediatric Surgery University Medical Center Göttingen Göttingen Germany. Ramaciotti Centre for Cryo-Electron Microscopy Monash University Melbourne Victoria Australia. Georg-August-University Göttingen Campus Institute Data Science Goettingen Germany. CCC-N (Comprehensive Cancer Center Lower Saxony) Göttingen Germany. Department of Hematology Oncology and Cancer Immunology (Campus Benjamin Franklin) Charité Universitätsmedizin Berlin Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Max Delbrück Center Berlin Germany. Clinical Research Unit 5002 KFO5002 University Medical Center Göttingen Germany. Georg-August-University Göttingen Institute of Computer Science Goettingen Germany. constantin.pape@informatik.uni-goettingen.de. Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC) Georg-August-University Göttingen Göttingen Germany. constantin.pape@informatik.uni-goettingen.de.

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Retraction notice to "A strategic review on sustainable approaches in municipal solid waste management and energy recovery: Role of artificial intelligence, economic stability and life cycle assessment" [Bioresour. Technol. 379 (2023) 129044]

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Bioresource technology 2025年 421卷 132185页

作者： Rajendiran Naveenkumar Jayaraj Iyyappan Ravichandran Pravin Seifedine Kadry Jeehoon Han Raveendran Sindhu Mukesh Kumar Awasthi Samuel Lalthazuala Rokhum Gurunathan Baskar Biological Systems Engineering University of Wisconsin-Madison Madison WI 53706 United States Forest Products Laboratory USDA Forest Service Madison WI 53726 United States. Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602107 India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119 India. Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Chemical Engineering Pohang University of Science and Technology Pohang 37673 Republic of Korea. Department of Food Technology TKM Institute of Technology Kollam Kerala India. College of Natural Resources and Environment Northwest A&F University Yangling Shaanxi Province 712100 China. Department of Chemistry National Institute of Technology Silchar 788010 Assam India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119 India Department of Applied Data Science Noroff University College Kristiansand Norway. Electronic address: basg2002@***.

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Regularized LTI System Identification with Multiple Regularization Matrix

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IFAC-PapersOnLine 2018年第15期51卷 180-185页

作者： Chen, Tianshi Andersen, Martin S. Mu, Biqiang Yin, Feng Ljung, Lennart Qin, S. Joe School of Science and Engineering and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Hong Kong Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark Department of Electrical Engineering Linköping University Linköping Sweden Department of Electrical Engineering Department of Chemical Engineering and Materials Science University of Southern California United States

Regularization methods with regularization matrix in quadratic form have received increasing attention. For those methods, the design and tuning of the regularization matrix are two key issues that are closely related. For systems with complicated dynamics, it would be preferable that the designed regularization matrix can bring the hyper-parameter estimation problem certain structure such that a locally optimal solution can be found efficiently. An example of this idea is to use the so-called multiple kernel Chen et al. (2014) for kernel-based regularization methods. In this paper, we propose to use the multiple regularization matrix for the filter-based regularization. Interestingly, the marginal likelihood maximization with the multiple regularization matrix is also a difference of convex programming problem, and a locally optimal solution could be found with sequential convex optimization techniques. © 2018

关键词： Convex optimization Identification (control systems) Linear systems Number theory Optimal systems Religious buildings Convex optimization techniques Difference of convex programming Hyper parameter Marginal likelihood Multiple kernels Optimal solutions Quadratic form Regularization methods

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Strain compensated robust semiconductor saturable absorber mirror for fiber lasers

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Chinese Optics Letters 2019年第7期17卷 59-62页

作者： Yan Wang Nan Lin Wanli Gao Huanyu Song Minglie Hu Haiming Li Wenxia Bao Xiaoyu Ma Zhigang Zhang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China National Research Center for Optoelectronic Engineering Institute of SemiconductorsChinese Academy of SciencesBeijing 100871China Ultrafast Laser Laboratory Key Laboratory of Opto-electronic Information Technical Science of Ministry of EducationSchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin 300072China Huakuai Photonic Technologies Inc. Zhongshan 528400China

We demonstrate a strain compensated long lifetime semiconductor saturable absorber mirror(SESAM) with a high modulation depth for fiber *** SESAM was measured to have a damage threshold of 9.5 m J/cm^2, a modulation depth of 11.5%, a saturation fluence of 39.3 μJ/cm^2, and an inversed saturable absorption coefficient of 630 m J/cm^*** SESAM has been applied to a linear cavity mode-locked Yb-doped fiber laser, which has been working for more than a year without damage of the SESAM.

关键词： Strain robust semiconductor saturable absorber mirror

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

Nature Water

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Nature Water 2025年 1-11页

作者： Aßmann, Eva Greiner, Timo Richard, Hugues Wade, Matthew Agrawal, Shelesh Amman, Fabian Böttcher, Sindy Lackner, Susanne Landthaler, Markus Mangul, Serghei Munteanu, Viorel Psomopoulos, Fotis Smith, Maureen Trofimova, Maria Ullrich, Alexander von Kleist, Max Wyler, Emanuel Hölzer, Martin Irrgang, Christopher Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London United Kingdom Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA United States Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE. © Springer Nature Limited 2025.

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On information-theoretic characterizations of markov random fields and subfields

On information-theoretic characterizations of markov random ...

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作者： Yeung, Raymond W. Al-Bashabsheh, Ali Chen, Chao Chen, Qi Moulin, Pierre Institute of Network Coding Chinese University of Hong Kong Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong Big Data and Brain Computing Center Beihang University Beijing100191 China State Key Laboratory of Integrated Service Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States Coordinated Science Laboratory University of Illinois UrbanaIL61801 United States

Let Xi, i ϵ V form a Markov random field (MRF) represented by an undirected graph G = (V,E) , and V′ be a subset of V. We determine the smallest graph that can always represent the subfield Xi, i ϵ V′ as an MRF. Based on this result, we obtain a necessary and sufficient condition for a subfield of a Markov tree to be also a Markov tree. When G is a path so that Xi, i ϵ V form a Markov chain, it is known that the I -Measure is always nonnegative (Kawabata and Yeung in 1992). We prove that Markov chain is essentially the only MRF such that the I -Measure is always nonnegative. By applying our characterization of the smallest graph representation of a subfield of an MRF, we develop a recursive approach for constructing information diagrams for MRFs. Our work is built on the set-theoretic characterization of an MRF (Yeung et al. in 2002). © 1963-2012 IEEE.

关键词： Markov processes

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Hybrid WDM and Optical-CDMA over Multi-Mode Fiber Transmission System based on Optical Vortex

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Journal of Physics: Conference Series 2021年第1期1755卷

作者： Alaan Ghazi S. A. Aljunid Syed Zulkarnain Syed Idrus R. Endut C. B. M. Rashidi N. Ali Aras Al-dawoodi Ahmed M. Fakhrudeen Alaa Fareed Teena Sharma School of Human Development & Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia. Computer Technical Engineering Alkitab University Iraq Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Malaysia. School of Microelectronic Engineering (SCCE) Universiti Malaysia Perlis Perlis Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Networks Department College of Computer Science and Information Technology Kirkuk University Iraq School of Computing University Utara Malaysia Malaysia Department of Electronics and Communication Engineering Malviya National Institute of Technology Jaipur India

The optical vortex has recently attracted scholars to implement it in optical tweezers, microscopy, optical communications, quantum information processing, optical trapping, and laser machining. Optical vortex beam applied classically that can be transferred to the transverse amplitude of a heralded single-photon, and optical vortex possesses a helical wavefront and carries orbital angular momentum. In this study, Optical vortex is applied in optical-CDMA (optical code-division multiple-access) in conjunction with WDM (wavelength division multiplexing). This mechanism aims to increase the capacity and security in optical communication significantly. The implementation of Laguerre-Gaussian (LG) modes with optical vortex based on one dimension zero cross-correlation (ZCC) code shows that mode coupling reduces effectively. Consequently, a positive increase in channel performance and response. Accordingly, the LG modes based on the 1D-ZCC code are investigated and propagated over multi-mode fiber (MMF) based on an optical vortex, which also substantially reduces channel effects. Consequently, all these attributes combined will result in a hybrid WDM-Optical-CDMA with an optical vortex system over MMF.

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