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A universal language for finding mass spectrometry data patterns

Nature Methods 2025年 1-8页

作者： Damiani, Tito Jarmusch, Alan K. Aron, Allegra T. Petras, Daniel Phelan, Vanessa V. Zhao, Haoqi Nina Bittremieux, Wout Acharya, Deepa D. Ahmed, Mohammed M. A. Bauermeister, Anelize Bertin, Matthew J. Boudreau, Paul D. Borges, Ricardo M. Bowen, Benjamin P. Brown, Christopher J. Chagas, Fernanda O. Clevenger, Kenneth D. Correia, Mario S. P. Crandall, William J. Crüsemann, Max Fahy, Eoin Fiehn, Oliver Garg, Neha Gerwick, William H. Gilbert, Jeffrey R. Globisch, Daniel Gomes, Paulo Wender P. Heuckeroth, Steffen James, C. Andrew Jarmusch, Scott A. Kakhkhorov, Sarvar A. Kang, Kyo Bin Kessler, Nikolas Kersten, Roland D. Kim, Hyunwoo Kirk, Riley D. Kohlbacher, Oliver Kontou, Eftychia E. Liu, Ken Lizama-Chamu, Itzel Luu, Gordon T. Luzzatto Knaan, Tal Mannochio-Russo, Helena Marty, Michael T. Matsuzawa, Yuki McAvoy, Andrew C. McCall, Laura-Isobel Mohamed, Osama G. Nahor, Omri Neuweger, Heiko Niedermeyer, Timo H. J. Nishida, Kozo Northen, Trent R. Overdahl, Kirsten E. Rainer, Johannes Reher, Raphael Rodriguez, Elys Sachsenberg, Timo T. Sanchez, Laura M. Schmid, Robin Stevens, Cole Subramaniam, Shankar Tian, Zhenyu Tripathi, Ashootosh Tsugawa, Hiroshi van der Hooft, Justin J. J. Vicini, Andrea Walter, Axel Weber, Tilmann Xiong, Quanbo Xu, Tao Pluskal, Tomáš Dorrestein, Pieter C. Wang, Mingxun Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czech Republic Metabolomics Core Facility Immunity Inflammation and Disease Laboratory Division of Intramural Research National Institute of Environmental Health Sciences National Institutes of Health Research Triangle Park NC United States Department of Chemistry and Biochemistry University of Denver Denver CO United States Functional Metabolomics Lab CMFI Cluster of Excellence University of Tuebingen Tuebingen Germany Department of Biochemistry University of California Riverside Riverside CA United States Department of Pharmaceutical Sciences Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado Anschutz Medical Campus Aurora CO United States Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA United States Biologicals and Natural Products Discovery Crop Protection R & D Corteva Agrisciences Indianapolis IN United States BioMolecular Sciences School of Pharmacy University of Mississippi Oxford MS United States Pharmacognosy Faculty of Pharmacy Al-Azhar University Nasr City Egypt Department of Fundamental Chemistry Institute of Chemistry University of São Paulo São Paulo Brazil Department of Chemistry Case Western Reserve University Cleveland OH United States Walter Mors Institute of Research on Natural Products Federal University of Rio de Janeiro Rio de Janeiro Brazil Environmental Genomics and Systems Biology Division Lawrence Berkeley National Lab Berkeley CA United States The Joint Genome Institute Lawrence Berkeley National Lab Berkeley CA United States Mass Spectrometry Center of Expertise Regulatory and Stewardship Corteva Agrisciences Indianapolis IN United States Biologicals and Natural Products Crop Protection R & D Corteva Agrisciences Indianapolis IN United States Department of Chemistry – BMC Science for Life Laborat

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized;most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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The EXPRES Stellar-Signals Project II. State of the Field in Disentangling Photospheric Velocities

arXiv

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arXiv 2022年

作者： Zhao, Lily L. Fischer, Debra A. Ford, Eric B. Wise, Alex Cretignier, Michaël Aigrain, Suzanne Barragan, Oscar Bedell, Megan Buchhave, Lars A. Camacho, João D. Cegla, Heather M. Cisewski-Kehe, Jessi Cameron, Andrew Collier de Beurs, Zoe L. Dodson-Robinson, Sally Dumusque, Xavier Faria, João P. Gilbertson, Christian Haley, Charlotte Harrell, Justin Hogg, David W. Holzer, Parker John, Ancy Anna Klein, Baptiste Lafarga, Marina Lienhard, Florian Maguire-Rajpaul, Vinesh Mortier, Annelies Nicholson, Belinda Palumbo, Michael L. Delgado, Victor Ramirez Shallue, Christopher J. Vanderburg, Andrew Viana, Pedro T.P. Zhao, Jinglin Zicher, Norbert Cabot, Samuel H.C. Henry, Gregory W. Roettenbacher, Rachael M. Brewer, John M. Llama, Joe Petersburg, Ryan R. Szymkowiak, Andrew E. Department of Astronomy Yale University 52 Hillhouse Ave. New HavenCT06511 United States Center for Computational Astrophysics Flatiron Institute Simons Foundation 162 Fifth Avenue New YorkNY10010 United States CCA Team Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Advanced Sciences PennState Team Astronomy Department University of Geneva 51 Chemin de Pegasi 51 Versoix1290 Switzerland Geneva Team Sub-department of Astrophysics Department of Physics University of Oxford OxfordOX1 3RH United Kingdom OxBridGen Team DTU Space National Space Institute Technical University of Denmark Elektrovej 328 Kgs. Lyngby DK-2800 Denmark Instituto de Astrofísica e Ciências do Espaço Universidade do Porto CAUP Rua das Estrelas Porto4150-762 Portugal Departamento de Física e Astronomia Faculdade de Ciências Universidade do Porto Rua do Campo Alegre 687 Porto4169-007 Portugal Porto Team Department of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom Centre for Exoplanets and Habitability University of Warwick CoventryCV4 7AL United Kingdom Warwick Team Department of Statistics University of Wisconsin-Madison 1300 University Ave. MadisonWI53706 United States University of St Andrews Centre for Exoplanet Science SUPA School of Physics & Astronomy North Haugh St AndrewsKY16 9SS United Kingdom St. Andrews Team Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology 77 Massachusetts Avenue 54-918 CambridgeMA02139 United States Department of Astronomy University of Texas at Austin 2515 Speedway AustinTX78712 United States ML EPRVs Team Dep

Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme precision radial velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The EXPRES Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed RV correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV RMS than classic linear decorrelation, but no method is yet consistently reducing the RV RMS to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets—such as solar data or data with known injected planetary and/or stellar signals—to better understand method performance and whether planetary signals are preserved. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrometers

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Likelihood-free inference with an improved cross-entropy estimator

arXiv

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arXiv 2018年

作者： Stoye, Markus Brehmer, Johann Louppe, Gilles Pavez, Juan Cranmer, Kyle Department of Physics Data Science Institute Imperial College London United Kingdom Center for Cosmology and Particle Physics Center for Data Science New York University New YorkNY United States Department of Electrical Engineering and Computer Science University of Liège Federico Santa María Technical University

We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joint likelihood ratio and joint score, conditioned on both observed and latent variables, can often be extracted from an implicit generative model or simulator to augment the training data for these surrogate models. We show how this augmented training data can be used to provide a new cross-entropy estimator, which provides improved sample efficiency compared to previous loss functions exploiting this augmented training data. Copyright © 2018, The Authors. All rights reserved.

关键词： Entropy

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Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

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ImmunoInformatics 2023年 9卷

作者： Pieter Meysman Justin Barton Barbara Bravi Liel Cohen-Lavi Vadim Karnaukhov Elias Lilleskov Alessandro Montemurro Morten Nielsen Thierry Mora Paul Pereira Anna Postovskaya María Rodríguez Martínez Jorge Fernandez-de-Cossio-Diaz Alexandra Vujkovic Aleksandra M. Walczak Anna Weber Rose Yin Anne Eugster Virag Sharma AUDACIS University of Antwerp Antwerp Belgium ADREM data lab Department of Computer Science University of Antwerp Antwerp Belgium Institute of Structural and Molecular Biology University of London London United Kingdom Department of Mathematics Imperial College London London United Kingdom National Institute for Biotechnology in the Negev Ben-Gurion University of the Negev Beer-Sheva Israel Department of Industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva Israel INSERM U932 PSL University Institut Curie Paris 75005 France Laboratoire de physique de l'Ecole normale superieure CNRS PSL University Sorbonne Universite Universite Paris-Cité Paris 75005 France Department of Physics University of Washington Seattle WA USA Department of Health Technology Technical University of Denmark Lyngby DK-2800 Denmark Sanofi R&D Chilly-Mazarin 91380 France Clinical Virology Unit Institute of Tropical Medicine Antwerp Belgium IBM Research Europe Säumerstrasse 4 Rüschlikon 8803 Switzerland Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA Center for Regenerative Therapies Dresden Faculty of Medicine TU Dresden Dresden Germany Department of Chemical Sciences School of Natural Sciences University of Limerick Limerick V94 T9PX Ireland

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

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Embedded whispering-gallery mode microsphere resonator in a tapered hollow annular core fiber

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Photonics Research 2018年第12期6卷 1124-1129页

作者： JIAWEI WANG XIAOBEI ZHANG MING YAN LEI YANG FENGYU HOU WEN SUN XIAOTONG ZHANG LIBO YUAN HAI XIAO TINGYUN WANG Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University The Key Laboratory of In-Fiber Integrated Optics Ministry of Education College of Science Harbin Engineering University School of Electronic Engineering and Automation Guilin University of Electronic Technology Department of Electrical and Computer Engineering Clemson University

We propose and demonstrate a tapered hollow annular core fiber (HACF) coupler for excitation of whisperinggallery modes (WGMs) of an embedded microsphere resonator. The coupler is simply fabricated by fusion splicing of a segment of HACF with the single-mode fiber (SMF), and then improved by tapering the splicing joint to reduce the cone-apex angle. Therefore, the coupling efficiency from the SMF to the HACF is enhanced to excite various WGMs via evanescent field coupling. Normal positive, negative symmetrical Lorentzian and asymmetric Fano line shapes can be obtained by varying the resonator size and location. Another interesting phenomenon is observed that a higher Q-factor mode in a lower Q-factor mode has a contrast as high as 58. Temperature sensing with good stability is also demonstrated. This embedded WGM microsphere resonator in the tapered HACF is expected to promote environmental adaptability in practical applications due to its simplicity and

关键词： Single mode fiber (SMF) cavity Embedded WGM microsphere resonator

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Flexible energy management protocol for cooperative EV-to-EV charging

arXiv

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arXiv 2018年

作者： Zhang, Rongqing Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China Department of Electrical and Computer Engineering Colorado State University CO United States Shenzhen Research Institute of Big Data Shenzhen Guangdong China

In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to- EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EVprovider- optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs. Copyright © 2018, The Authors. All rights reserved.

关键词： Electric vehicles

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Operational Research: Methods and Applications

arXiv

引用

arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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