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Communications in Computer and Information Science 2016年 598卷 123-143页

作者： Fuchs, Johannes Jäckle, Dominik Weiler, Niklas Schreck, Tobias Data Analysis and Visualization Group University of Konstanz Universitätstr. 10 Konstanz78457 Germany

In exploratory data analysis, important analysis tasks include the assessment of similarity of data points, labeling of outliers, identifying and relating groups in data, and more generally, the detection of patterns. Specifically, for large data sets, such tasks may be effectively addressed by glyph-based visualizations. Appropriately defined glyph designs and layouts may represent collections of data to address these aforementioned tasks. Important problems in glyph visualization include the design of compact glyph representations, and a similarityor structure-preserving 2D layout. Projection-based techniques are commonly used to generate layouts, but often suffer from over-plotting in 2D display space, which may hinder comparing and relating tasks. Inspired by contour and venation shapes of natural leafs, and their aggregation by stems, we introduce a novel glyph design for visualizing multi-dimensional data. Motivated by the human ability to visually discriminate natural shapes like trees in a forest, single flowers in a flowerbed, or leaves at shrubs, we design a flexible leaf-shaped data glyph, where data controls main leaf properties including leaf morphology, leaf venation, and leaf boundary shape. Our basic leaf glyph can map to more than a dozen of numeric and categorical variables. We also define custom visual aggregation schemes to scale the glyph for large numbers of data records, including prototype-based, set-based, and hierarchic aggregation. We show by example that our design is effectively interpretable to solve multivariate data analysis tasks, and provides effective data mapping. The design provides an aesthetically pleasing appearance, and lends itself easily to storytelling in environmental data analysis problems, among others. The glyph and its aggregation schemes are proposed as a scalable multivariate data visualization design, with applications in data visualization for mass media and data journalism, among others. © Springer International

关键词： visualization

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100 essential questions for the future of agriculture

Modern Agriculture

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Modern Agriculture 2023年第1期1卷

作者： Yuming Hu Taolan Zhao Yafang Guo Meng Wang Kerstin Brachhold Chengcai Chu Andrew Hanson Sachin Kumar Rongcheng Lin Wenjin Long Ming Luo Jian Feng Ma Yansong Miao Shaoping Nie Yu Sheng Weiming Shi James Whelan Qingyu Wu Ziping Wu Wei Xie Yinong Yang Chao Zhao Lei Lei Yong-Guan Zhu Qifa Zhang Wiley-VCH GmbH Weinheim Germany Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (equal) Project administration (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Investigation (equal) Resources (equal) Writing - original draft (equal) Contribution: Data curation (equal) Investigation (equal) Visualization (equal) Contribution: Writing - review & editing (equal) State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources College of Agriculture South China Agricultural University Guangzhou China Contribution: Investigation (equal) Resources (equal) Horticultural Sciences Department University of Florida Gainesville Florida USA Rumen Biotechnology Laboratory Animal Nutrition Division ICAR-National Dairy Research Institute Karnal Haryana India Key Laboratory of Photobiology Institute of Botany Chinese Academy of Sciences Beijing China School of Economics and Management China Agricultural University Beijing China Guangdong Provincial Key Laboratory of Applied Botany & Agriculture and Biotechnology Research Center South China Botanical Garden Chinese Academy of Sciences Guangzhou China Institute of Plant Science and Resources Okayama University Kurashiki Japan School of Biological Sciences Nanyang Technological University Singapore City Singapore State Key Laboratory of Food Science and Technology China-Canada Joint Lab of Food Science and Technology (Nanchang) Nanchang University Nanchang China China Center for Agricultural Policy School of Advanced Agricultural Sciences Peking University Beijing China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing China State Key Laboratory of Plant Physiology and Biochemistry College of Life Sciences Zhejiang University Hangzhou Zhejiang China Department of Animal Plant and Soil Sciences La Trobe University Me

The world is at a crossroad when it comes to agriculture. The global population is growing, and the demand for food is increasing, putting a strain on our agricultural resources and practices. To address this challenge, innovative, sustainable, and inclusive approaches to agriculture are urgently required. In this paper, we launched a call for Essential Questions for the Future of Agriculture and identified a priority list of 100 questions. We focus on 10 primary themes: transforming agri-food systems, enhancing resilience of agriculture to climate change, mitigating climate change through agriculture, exploring resources and technologies for breeding, advancing cultivation methods, sustaining healthy agroecosystems, enabling smart and controlled-environment agriculture for food security, promoting health and nutrition-driven agriculture, exploring economic opportunities and addressing social challenges, and integrating one health and modern agriculture. We emphasise the critical importance of interdisciplinary and multidisciplinary research that integrates both basic and applied sciences and bridges the gaps among various stakeholders for achieving sustainable agriculture.

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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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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Self organizing maps for the visual analysis of pitch contours 20

Self organizing maps for the visual analysis of pitch contou...

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20th Nordic Conference of Computational Linguistics, NODALIDA 2015

作者： Sacha, Dominik Asano, Yuki Rohrdantz, Christian Hamborg, Felix Keim, Daniel Braun, Bettina Butt, Miriam Data Analysis and Visualization Group Department of Linguistics University of Konstanz Germany

ISBN: (纸本)9789175190983

We present a novel interactive approach for the visual analysis of intonation contours. Audio data are processed algorithmically and presented to researchers through interactive visualizations. To this end, we automatically analyze the data using machine learning in order to find groups or patterns. These results are visualized with respect to meta-data. We present a flexible, interactive system for the analysis of prosodic data. Using realworld application examples, one containing preprocessed, the other raw data, we demonstrate that our system enables researchers to interact dynamically with the data at several levels and by means of different types of visualizations, thus arriving at a better understanding of the data via a cycle of hypothesis generation and testing that takes full advantage of our visual processing abilities. © NODALIDA *** right reserved.

关键词： Self organizing maps

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Efficient dense reconstruction using geometry and image consistency constraints

Efficient dense reconstruction using geometry and image cons...

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IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015

作者： Shashkov, Mikhail M. Mak, Jason Recker, Shawn Nguyen, Connie Owens, John Joy, Kenneth I. Institute for Data Analysis and Visualization University of California - Davis DavisCA95616 United States

ISBN: (纸本)9781467395588

We introduce a method for creating very dense reconstructions of datasets, particularly turn-table varieties. The method takes in initial reconstructions (of any origin) and makes them denser by interpolating depth values in two-dimensional image space within a superpixel region and then optimizing the interpolated value via image consistency analysis across neighboring images in the dataset. One of the core assumptions in this method is that depth values per pixel will vary gradually along a gradient for a given object. As such, turntable datasets, such as the dinosaur dataset, are particularly easy for our method. Our method modernizes some existing techniques and parallelizes them on a GPU, which produces results faster than other densification methods. © 2015 IEEE.

关键词： Phonographs

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Field-induced reentrant magnetoelectric phase in LiNiPO4

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Physical Review B 2017年第6期95卷 064421-064421页

作者： Rasmus Toft-Petersen Ellen Fogh Takumi Kihara Jens Jensen Katharina Fritsch Jooseop Lee Garrett E. Granroth Matthew B. Stone David Vaknin Hiroyuki Nojiri Niels Bech Christensen Helmholtz Zentrum Berlin für Materialien und Energie D-14109 Berlin Germany Department of Physics Technical University of Denmark DK-2880 Kongens Lyngby Denmark Institute for Materials Research Tohoku University Sendai 980-8577 Japan Niels Bohr Institute Universitetsparken 5 DK-2100 Copenhagen Denmark Quantum Condensed Matter Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Ames Laboratory and Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA

Using pulsed magnetic fields up to 30T we have measured the bulk magnetization and electrical polarization of LiNiPO4 and have studied its magnetic structure by time-of-flight neutron Laue diffraction. Our data establish the existence of a reentrant magnetoelectric phase between 19T and 21T. We show that a magnetized version of the zero field commensurate structure explains the magnetoelectric response quantitatively. The stability of this structure suggests a field-dependent spin anisotropy. Above 21T, a magnetoelectrically inactive, short-wavelength incommensurate structure is identified. Our results demonstrate the combination of pulsed fields with epithermal neutron Laue diffraction as a powerful method to probe even complex phase diagrams in strong magnetic fields.

关键词： Magnetoelectric effect Electronically polarized systems Magnetization measurements Neutron diffraction

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Hematologic and spirometric characteristics of Tajik and Kyrgyz highlanders in the Pamir Mountains

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American Journal of Human Biology 2020年第2期33卷

作者： Xierzhatijiang Sulaiman Wei-fang Xu Liu-Hong Cai Xiao-Yang Huang Lu-Feng Cheng Ya-Ping Zhang Department of Pharmacology School of Pharmacy Xinjiang Medical University Urumqi Xinjiang China Contribution: Data curation Formal analysis Investigation Visualization Writing - original draft Writing - review & editing Shenzhen Hospital of Guangzhou University of Chinese Medicine Shenzhen Guangdong China Contribution: Conceptualization Funding acquisition Investigation Chengdu Yumei Hospital Chengdu Sichuan China Contribution: Investigation The People's Hospital of Shawan County Shawan Xinjiang China Ya-Ping Zhang KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases Kunming Institute of Zoology Chinese Academy of Sciences 32 East Jiaochang Road Kunming Yunnan 650223 China. Contribution: Conceptualization Funding acquisition Methodology Supervision State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China State Key Laboratory for Conservation and Utilization of Bio-Resources Yunnan University Kunming Yunnan China Kunming College of Life Science University of Chinese Academy of Sciences Kunming Yunnan China KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming Yunnan China Lu-Feng Cheng Department of Pharmacology School of Pharmacy Xinjiang Medical University 393 Xinyi Road Urumqi Xinjiang 830054 China.

Objectives In this study, we measured the hematologic and spirometric parameters of native Tajik and Kyrgyz highlanders in the Pamir Mountains to investigate adaptations to high altitude stressors. Methods Hematological parameters including arterial oxygen saturation (SaO 2 ), red blood cell (RBC) counts, and hemoglobin (Hb) concentration were measured on Sarikoli Tajik (n = 80; 3100 m), Wakhi Tajik (n = 48; 3500 m), and Kyrgyz (n = 64; 3250 m) in comparison to lowland Uyghurs (n = 50; 1300 m). Spirometric parameters including forced vital capacity (FVC), the first second of forced expiration (FEV1), and forced expiratory flow between 25% and 75% (FEF25-75) were measured. We also reported mountain sickness symptoms in these highlanders and conducted a multivariate regression analysis to analyze the association between these symptoms and the measured parameters. Results SaO 2 of Sarikoli Tajik, Wakhi Tajik, and Kyrgyz (91%-93.5%) are significantly lower than lowland Uyghurs, yet are comparable to other native highlanders at a similar altitude. RBC counts and Hb concentrations of all three highland populations are significantly increased compared to Uyghurs. FVC is lower in Sarikoli Tajik, Wakhi Tajik, and Kyrgyz (male: 3.48-3.86 L, female: 2.47-2.78 L) compared to Uyghurs. Combined with normal FEV1, elevated FEV1/FVC ratio, and FEF25-75, the spirometric patterns of these highlanders indicate restrictive lung disease. A high prevalence of mountain sickness symptoms such as headache and nausea was found in all three highland populations, and are attributed to low FVC and aging by regression analysis. Conclusion Tajik and Kyrgyz highlanders showed adaptation in SaO 2 , RBC, and Hb level, but poor performance in spirometry, which causes mountain sickness.

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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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The momentum distribution of liquid 4He

arXiv

引用

arXiv 2017年

作者： Prisk, T.R. Bryan, M.S. Sokol, P.E. Granroth, G.E. Moroni, S. Boninsegni, M. Center for Neutron Research National Institute of Standards and Technology GaithersburgMD20899-6100 United States Department of Physics Indiana University BloomingtonIN47408 United States Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak RidgeTN37831 United States CNR-IOM DEMOCRITOS Istituto Officina dei Materiali SISSA Scuola Internazionale Superiore di Studi Avanzati Via Bonomea 265 TriesteI-34136 Italy Department of Physics University of Alberta EdmontonABT6G 2E1 Canada

We report high-resolution neutron Compton scattering measurements of liquid 4He under saturated vapor pressure. There is excellent agreement between the observed scattering and ab_initio predictions of its lineshape. Quantum Monte Carlo calculations predict that the Bose condensate fraction is zero in the normal fluid, builds up rapidly just below the superfluid transition temperature, and reaches a value of approximately 7.5% below 1 K. We also used model fit functions to obtain from the scattering data empirical estimates for the average atomic kinetic energy and Bose condensate fraction. These quantities are also in excellent agreement with ab_initio calculations. The convergence between the scattering data and Quantum Monte Carlo calculations is strong evidence for a Bose broken symmetry in superfluid 4He. Copyright © 2017, The Authors. All rights reserved.

关键词： Kinetics

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