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A Green Paradigm for Internet of Things: Ambient Backscatter Communications

China Communications 2019年第7期16卷 109-119页

作者： Wei Zhang Yao Qin Wenjing Zhao Minzheng Jia Qiang Liu Ruisi He Bo Ai Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology and also with Institute of Telecommunication Satellites China Academy of Space Technology (CAST) Beijing Key Lab of Transportation Data Analysis and Mining Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Information Engineering Beijing Polytechnic College Beijing 100042 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China

Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.

关键词： ambient backscatter (AmBC) RF-powered device Internet of Things (IoT) battery-free tag wire-less sensor

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Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing

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ImmunoInformatics 2022年 5卷

作者： Sebastiaan Valkiers Nicky de Vrij Sofie Gielis Sara Verbandt Benson Ogunjimi Kris Laukens Pieter Meysman Adrem Data Lab Department of Computer Science University of Antwerp Antwerp Belgium Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS) University of Antwerp Antwerp Belgium Clinical Immunology Unit Department of Clinical Sciences Institute of Tropical Medicine Antwerp Belgium Molecular Digestive Oncology Department of Oncology Katholieke Universiteit Leuven Leuven Belgium Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID) Vaccine & Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium Antwerp Center for Translational Immunology and Virology (ACTIV) Vaccine and Infectious Disease Institute (VAXINFECTIO) University of Antwerp Antwerp Belgium Department of Paediatrics Antwerp University Hospital Antwerp Belgium

T cells exercise a multitude of functions such as cytotoxicity, secretion of immunomodulating cytokines or regulation of tolerance, collectively resulting in an effective control of immune-related disease. Through the unique mechanism of V(D)J recombination, T cells express a highly specific receptor complex known as the T-cell receptor (TCR). Single-cell sequencing technologies have paved the road for interrogating the transcriptome and the paired αβ TCR repertoire of a single T cell in tandem. In contrast, conventional bulk methods are restricted to only one layer of information. This combination of transcriptomic- and repertoire information can provide novel insight into the functional character of T cell immunity. Recently, single-cell technologies have gained in popularity due to improvements in throughput, decrease in cost and the ability for multimodal experiments that integrate different information layers. Consequently, this prompts the need for the development of novel computational tools that integrate transcriptomic profiles and corresponding features of the TCR repertoire. Here we discuss the current progress in the field of single-cell T cell sequencing, with a focus on the multimodality of new approaches that allow the paired profiling of cellular phenotype and clonotype information. In addition, this review provides detailed descriptions of recent computational developments for analyzing single-cell TCR sequencing data in an integrative manner using novel computational approaches. Finally, we present an overview of the available software tools that can be used to perform integrative analysis of gene expression and TCR profiles.

关键词： Single-cell sequencing Single-cell immune profiling Single-cell gene expression T-cell receptor repertoire Multi-omics

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Spatiotemporally Targeted Polypeptide Nanoantidotes Improve Chemotherapy Tolerance of Cisplatin

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Angewandte Chemie 2022年第47期134卷

作者： Dr. Jiazhen Yang Dr. Tianqi Su Dr. Haoyang Zou Prof. Guanqing Yang Prof. Jianxun Ding Prof. Xuesi Chen Key Laboratory of Polymer Ecomaterials Changchun Institute of Applied Chemistry Chinese Academy of Sciences 5625 Renmin Street Changchun 130022 P. R. China School of Applied Chemistry and Engineering University of Science and Technology of China 96 Jinzhai Road Hefei 230026 P. R. China Contribution: Conceptualization (equal) Formal analysis (lead) Investigation (lead) Methodology (lead) Resources (equal) Validation (equal) Visualization (equal) Writing - original draft (lead) Department of General Surgery The Second Hospital of Jilin University 218 Ziqiang Street Changchun 130041 P. R. China Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (equal) Resources (equal) Software (equal) Validation (equal) Contribution: Data curation (lead) Formal analysis (equal) Funding acquisition (supporting) Investigation (supporting) Validation (equal) Visualization (equal) Writing - review & editing (equal) Contribution: Data curation (lead) Funding acquisition (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Data curation (equal) Formal analysis (supporting) Funding acquisition (lead) Investigation (supporting) Project administration (lead) Supervision (lead) Writing - review & editing (lead) Contribution: Conceptualization (equal) Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - review & editing (lead)

The toxicity of drugs causes various adverse effects in patients. While antidotes that neutralize drug toxicity help reduce systemic damage during clinical therapy, these antidotes are generally accompanied by the loss of drug efficacy. Herein, the spatiotemporally targeted polycystine-based nanoantidotes were designed as a neutralizer of cisplatin (CDDP) to decrease its toxicity without affecting its anticancer efficacy. The nanoantidotes administered before CDDP selectively accumulated in the liver and kidney and then firmly bound to CDDP through the highly stable Pt−S bond during subsequent chemotherapy. This two-step administration strategy reduced the level of Pt in normal organs, shortened the half-life of CDDP in plasma, and increased the tolerance to CDDP. More importantly, the nanoantidotes maintained the anticancer efficacy of CDDP after reducing systemic toxicity, indicating its great potential in expanding the clinical application of CDDP.

关键词： polypeptides spatiotemporal targeting nanoantidotes drug tolerance chemotherapy

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A systematic review of challenges and proposed solutions in modeling multimodal data

arXiv

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arXiv 2025年

作者： Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine Institute of General Practice/Family Medicine Faculty of Medicine Medical Center University of Freiburg Elsässer Straße 2M Freiburg79110 Germany Freiburg Center for Data Analysis Modeling and AI University of Freiburg Ernst-Zermelo-Str. 1 Freiburg79104 Germany Institute of Medical Biometry and Statistics Faculty of Medicine Medical Center University of Freiburg Stefan-Meier-Straße 26 Freiburg79104 Germany Institute for Applied Medical Informatics University Medical Center Hamburg-Eppendorf Martinistraße 52 Hamburg20251 Germany Institute of Medical Systems Biology Ulm University Helmholtzstraße 16 Ulm89081 Germany Department of Computer Science Faculty of Engineering University of Freiburg Georges-Köhler-Allee 051 Freiburg79110 Germany

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. © 2025, CC BY.

关键词： Clinical research

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Implications of Sea Breezes on Air Quality Monitoring in a Coastal Urban Environment: Evidence From High Resolution Modeling of NO2and O3

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Journal of Geophysical Research: Atmospheres 2023年第11期128卷 e2022JD037860-e2022JD037860页

作者： Bo Wang Jeffrey A. Geddes Taylor J. Adams Elena S. Lind Brian C. McDonald Jian He Colin Harkins Dan Li Gabriele G. Pfister Department of Earth and Environment Boston University MA Boston USA B. Wang Contribution: Conceptualization Methodology Formal analysis Investigation Resources Writing - original draft Writing - review & editing Visualization Contribution: Conceptualization Methodology Formal analysis Investigation Resources Data curation Writing - review & editing Supervision Project administration Funding acquisition Contribution: Resources Writing - review & editing Department of Electrical and Computer Engineering Virginia Tech VA Blacksburg USA Now at NASA Goddard Space Flight Center Greenbelt MD USA Chemical Sciences Laboratory NOAA Earth System Research Laboratories CO Boulder USA Cooperative Institute for Research in Environmental Sciences University of Colorado CO Boulder USA Contribution: Methodology Resources Writing - review & editing Atmospheric Chemistry Observations and Modeling Laboratory NCAR CO Boulder USA Contribution: Conceptualization Resources Writing - review & editing

Coastal urban environments face unique challenges associated with air quality-meteorology interactions. In this study, high resolution chemical transport modeling over the Greater Boston area was performed to improve our understanding of sea breezes impacts on the spatiotemporal variability of primary and secondary pollutants. We perform WRF-Chem simulations at 3 km resolution over June 22 to 10 July 2019 (a period that included 10 sea breeze occurrences), and use Pandora tropospheric NO 2 column, surface air quality monitoring, and vertical meteorological aircraft profiles for evaluation. The model generally reproduces observed spatiotemporal variability of air pollution during sea breezes well. Tropospheric columns of NO 2 predicted by the model and observed by the Pandora instrument show that sea breezes are associated with rapid increases and steep gradients in tropospheric NO 2 and confirm accumulation of local primary emissions. Spatial heterogeneity in tropospheric NO 2 is strongly governed by inland penetration lengths of the sea breeze front. Process diagnostics show that three sea-breeze days where O 3 observations recorded hourly concentrations >70 ppb have both efficient net chemical O 3 production in the boundary layer (>10 ppb/hr) and rapid O 3 convergence in the near-surface convergence zone (>20 ppb/hr). During sea breezes, interactions between photochemistry, the convergence zone inland penetration, and urban NO x titration effects, contribute to strong heterogeneity and high O 3 inland that is not captured by the current monitoring network. We discuss monitoring needs and model applications for the sea breeze scenarios, with broad implications for air quality monitoring in coastal urban environments. High resolution WRF-Chem modeling reproduces observed spatiotemporal variability of NO 2 and O 3 during sea breezes in the Greater Boston area Model results suggest high O 3 pollution and NO 2 column spatial variability associated with sea breezes is n

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Search for the doubly charmed baryon Ω_(cc)^(+)

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science China(Physics,Mechanics & Astronomy) 2021年第10期64卷 2-13页

作者： LHCb Collaboration Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing 100084 China Institute Of High Energy Physics (IHEP) Beijing 100039 China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing 100091 China University of Chinese Academy of Sciences Beijing 100086 China Institute of Particle Physics Central China Normal University Wuhan 430079 China CNRS IN2P3-LAPP Univ. Savoie Mont Blanc Annecy France CNRS/IN2P3 LPC Université Clermont Auvergne Clermont-Ferrand France CNRS/IN2P3 CPPM Aix Marseille Univ Marseille France CNRS/IN2P3 IJCLab Université Paris-Saclay Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Max-Planck-Institut für Kernphysik (MPIK) Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy Universita degli Studi di Padova Universita e INFN Padova Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics and VU Unive

A search for the doubly charmed baryon ?cc+ with the decay mode c Ω_(cc)^(+)→ Ξc+K-π+ is performed using proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the LHCb experiment from 2016 to 2018, corresponding to an integrated luminosity of 5.4 fb-1. No significant signal is observed within the invariant mass range of 3.6 to 4.0 GeV/c2. Upper limits are set on the ratio R of the production cross-section times the total branching fraction of the Ω_(cc)^(+)→ Ξc+K-π+ decay with respect to the Ξcc++→ Λc+K-π+π+ decay. Upper limits at 95% credibility level for R in the range 0.005 to 0.11 are obtained for diferent hypotheses on the ?cc+ mass and lifetime in the rapidity range from 2.0 to 4.5 and transverse momentum range from 4 to 15 GeV/c.

关键词： charmed baryons limits on production of particles charmed quarks experimental tests

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Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge

arXiv

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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 the doubly charmed baryon ?cc+

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science China(Physics,Mechanics & Astronomy) 2021年第10期Mechanics & Astronomy) .卷 6-17页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. Aidala S. Aiola Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero Z. Aliouche G. Alkhazov P. Alvarez Cartelle S. Amato Y. Amhis L. An L. Anderlini A. Andreianov M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista Leite R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels G. Bassi B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella A. Bertolin C. Betancourt F. Betti I. Bezshyiko S. Bhasin J. Bhom L. Bian M. S. Bieker S. Bifani P. Billoir M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti M. Bj?rn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska J. A. Boelhauve O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu A. Buonaura C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes P. Campana A. F. Campoverde Quezada S. Capelli L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti L. Carus K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani J. Cerasoli A. J. Chadwick M. G. Chapman M. Charles P. Charpentier G. Chatzikonstantinidis C. A. Chavez Barajas M. Chefdeville C. Chen S. Chen A. Chernov V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek P. E. L. Clarke M. Clemencic H. V. Clif J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras L. Nikhef National Institute for Subatomic Physics Physik-Institut Universit?t Zürich Oliver Lodge Laboratory University of Liverpool Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela H.H. Wills Physics Laboratory University of Bristol Université Clermont Auvergne University of Michigan Ann Arbor United States INFN Sezione di Milano University of Cincinnati Fakult?t Physik Technische Universit?t Dortmund European Organization for Nuclear Research (CERN) School of Physics and Astronomy University of Glasgow ICCUB Universitat de Barcelona Department of Physics and Astronomy University of Manchester Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Cavendish Laboratory University of Cambridge Universidade Federal do Rio de Janeiro (UFRJ) Université Paris-Saclay INFN Sezione di Firenze INFN Sezione di Ferrara Physikalisches Institut Ruprecht-Karls-Universit?t Heidelberg Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Syracuse University Yandex School of Data Analysis Aix Marseille Univ Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Department of Physics University of Warwick Centro Brasileiro de Pesquisas Físicas (CBPF) Imperial College London INFN Sezione di Genova National University of Science and Technology “MISIS” I. Physikalisches Institut RWTH Aachen University INFN Sezione di Pisa INFN Sezione di Cagliari Institute of Nuclear Physics Moscow State University (SINP MSU) Institute of Theoretical and Experimental Physics NRC Kurchatov Institute (ITEP NRC KI) INFN Laboratori Nazionali di Frascati LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité Universita degli Studi di Padova Universita e INFN Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences School of Physics and Technology Wuhan University University of Birmingham Università di Modena e Reggio Emilia Department of Physics U

A search for the doubly charmed baryon ?cc+with the decay mode ?cc+→Ξc+K-π+is performed using proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the LHCb experiment from 2016 to 2018, corresponding to an integrated luminosity of 5.4 fb-1. No significant signal is observed within the invariant mass range of 3.6 to 4.0 GeV/c2. Upper limits are set on the ratio R of the production cross-section times the total branching fraction of the ?cc+→Ξc+K-π+decay with respect to the Ξcc++→Λc+K-π+π+decay. Upper limits at 95% credibility level for R in the range 0.005 to 0.11 are obtained for diferent hypotheses on the ?cc+mass and lifetime in the rapidity range from 2.0 to 4.5 and transverse momentum range from 4 to 15 GeV/c.

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Correction: Micronutrient intake and telomere length: findings from the UK Biobank

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European journal of nutrition 2024年第1期64卷 53页

作者： Marianna Spinou Androniki Naska Christopher P Nelson Veryan Codd Nilesh J Samani Vasiliki Bountziouka Department of Hygiene Epidemiology and Medical Statistics School of Medicine National and Kapodistrian University of Athens Athens Greece. Department of Cardiovascular Sciences University of Leicester Leicester UK. NIHR Leicester Biomedical Research Centre Glenfield Hospital Leicester UK. Department of Cardiovascular Sciences University of Leicester Leicester UK. vboun@aegean.gr. NIHR Leicester Biomedical Research Centre Glenfield Hospital Leicester UK. vboun@aegean.gr. Computer Simulation Genomics and Data Analysis Laboratory Department of Food Science and Nutrition School of the Environment University of the Aegean Ierou Lochou 10 & Makrygianni Lemnos 81400 Greece. vboun@aegean.gr. Population Policy and Practice Research and Teaching Department GOS Institute of child health UCL London UK. vboun@aegean.gr.

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analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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