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European radiology 2022年第7期32卷 5034页

作者： Jianhong Cheng John Sollee Celina Hsieh Hailin Yue Nicholas Vandal Justin Shanahan Ji Whae Choi Thi My Linh Tran Kasey Halsey Franklin Iheanacho James Warren Abdullah Ahmed Carsten Eickhoff Michael Feldman Eduardo Mortani Barbosa Ihab Kamel Cheng Ting Lin Thomas Yi Terrance Healey Paul Zhang Jing Wu Michael Atalay Harrison X Bai Zhicheng Jiao Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University 932 Lushan S Rd Yuelu District Changsha Hunan China. Department of Diagnostic Radiology Rhode Island Hospital 593 Eddy St. Providence RI 02903 USA. Warren Alpert Medical School of Brown University Providence RI 02903 USA. Department of Diagnostic Radiology Perelman School of Medicine at the University of Pennsylvania Philadelphia PA 19104 USA. Department of Data Science University of London London UK. Center for Biomedical Informatics Brown University Providence RI 02912 USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine at the University of Pennsylvania Philadelphia PA 19104 USA. Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine 601 N Caroline St Baltimore MD 21205 USA. Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine 601 N Caroline St Baltimore MD 21205 USA. hbai7@jh.edu.

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Molecular and Serologic Diagnostic Technologies for SARS-CoV-2

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arXiv 2022年

作者： Rando, Halie M. Brueffer, Christian Lordan, Ronan Dattoli, Anna Ada Manheim, David Meyer, Jesse G. Mundo, Ariel I. Perrin, Dimitri Mai, David Wellhausen, Nils Gitter, Anthony Greene, Casey S. Department of SystemsPharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and MolecularGenetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Clinical Sciences Lund University Lund Sweden Institute for TranslationalMedicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-5158 United States Department of Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Pathology Laboratory Medicine The Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Systems Pharmacology & Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States 1DaySooner Delaware United States Communication Research Center School of Public Health University of Haifa Haifa Israel Technion Israel Institute of Technology Haifa Israel Department of Biochemistry Medical College of Wisconsin MilwaukeeWI United States Department of Biomedical Engineering University of Arkansas FayettevilleAR United States School of Computer Science Queensland University of Technology Brisbane Australia Centre for Data Science Queensland University of Technology Brisbane Australia Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Center for Cellular Immunotherapies Perelman School of Medicine Parker Institute for Cancer Immunotherapy University of Pennsylvania PhiladelphiaPA United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Medical

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic. © 2022, CC BY.

关键词： SARS

Intelligent system for cultural objects identification, damage assessment and restoration

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Intelligent system for cultural objects identification, dama...

作者： Sakkopoulos, Evangelos Pikoulis, Erion-Vasilis Viennas, Emmanouil Nodarakis, Nikolaos Cheilakou, Eleni Saint, Amani-Christiana Koui, Maria Tsakalidis, Athanasios Graphics Multimedia and GIS System Lab Computer Engineering and Informatics Department University of Patras Rio-PatrasGR-26504 Greece Department of Materials Science and Engineering NDT Lab School of Chemical Engineering National Technical University of Athens Greece

Cultural objects and art works need ongoing conservation interventions in order to be available for the next generations. The most object-friendly analysis approaches are based on non-destructive techniques (NDTs) that allow both the materials characterisation as well as the decay detection of cultural artefacts. Non-destructive testing and evaluation includes the employment of several methods such as the well-established technique of diffuse reflectance spectroscopy with fibre optics (FORS). Such techniques produce output with multiple series of data for multiple different pigment used in objects. In this work, we present a data management solution that contributes with: 1) a library of known reference pigments/colours;2) a proposed pattern matching technique that allows the automatic classification of any new pigment. The experimental evaluation results show that the data processing proposed is effective. Feedback is particularly encouraging as it allows automation and therefore radically decreased time for pigment/colour matching and identification. © 2017 Inderscience Enterprises Ltd.

关键词： Reflection

Advancing image understanding in poor visibility environments: A collective benchmark study

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye Ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, Di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, Ruizhe Yi, Dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Expanding the active inference landscape: More intrinsic motivations in the perception-action loop

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arXiv 2018年

作者： Biehl, Martin Guckelsberger, Christian Salge, Christoph Smith, Simon C. Polani, Daniel Araya Inc. Tokyo Japan Computational Creativity Group Department of Computing Goldsmiths University of London London United Kingdom Game Innovation Lab Department of Computer Science and Engineering New York University New York CityNY United States Sepia Lab Adaptive Systems Research Group Department of Computer Science University of Hertfordshire Hatfield United Kingdom Institute of Perception Action and Behaviour School of Informatics University of Edinburgh United Kingdom

Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explana-tions for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g. different environments or agent morphologies. In the liter- A ture, paradigms that share this independence have been summarised under the notion of intrinsic motivations. In general and in contrast to active inference, these models of motivation come without a commitment to particular inference and action selection mechanisms. In this article, we study if the inference and action selection machinery of active inference can also be used by alternatives to the originally included intrinsic motivation. The perception-action loop explicitly relates inference and action selection to the environment and agent memory, and is consequently used as foundation for our analysis. We reconstruct the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original fea-tures intact. Furthermore, we illustrate the connection to universal reinforcement learning by means of our formalism. Active inference research may profit from comparisons of the dynamics induced by alternative intrinsic motivations. Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that also share the biological plausibility of active inference.62F15, 91B06 Copyright © 2018, The Authors. All rights reserved.

关键词： Profitability

Multi-tenant cross-slice resource orchestration: A deep reinforcement learning approach

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arXiv 2018年

作者： Chen, Xianfu Zhao, Zhifeng Wu, Celimuge Bennis, Mehdi Liu, Hang Ji, Yusheng Zhang, Honggang VTT Technical Research Centre of Finland Oulu Finland Zhejiang Lab College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Centre for Wireless Communications University of Oulu Finland Department of Electrical Engineering and Computer Science Catholic University of America United States Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

An approach for weakly-supervised deep information retrieval

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arXiv 2017年

作者： MacAvaney, Sean Hui, Kai Yates, Andrew Information Retrieval Lab Department of Computer Science Georgetown University Max Planck Institute for Informatics Saarbrücken Graduate School of Computer Science

Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that—given a weak training set of pseudo-queries, documents, relevance information—filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article’s headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection. Copyright © 2017, The Authors. All rights reserved.

关键词： Benchmarking

Choosing Requirements for Experimentation with User Interfaces of Requirements Modeling Tools

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arXiv 2017年

作者： Ghazi, Parisa Hossein Abad, Zahra Shakeri Glinz, Martin Department of Informatics University of Zurich Switzerland SEDS Lab Department of Computer Science University of Calgary Canada

—When designing a new presentation front-end called FlexiView for requirements modeling tools, we encountered a general problem: designing such an interface requires a lot of experimentation which is costly when the code of the tool needs to be adapted for every experiment. On the other hand, when using simplified user interface (UI) tools, the results are difficult to generalize. To improve this situation, we are developing an UI experimentation tool which is based on so-called ImitGraphs. ImitGraphs can act as a simple, but accurate substitute for a modeling tool. In this paper, we define requirements for such an UI experimentation tool based on an analysis of the features of existing requirements modeling tools. Copyright © 2017, The Authors. All rights reserved.

关键词： User interfaces

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

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arXiv 2023年

作者： labella, Dominic Adewole, Maruf Alonso-Basanta, Michelle Altes, Talissa Anwar, Syed Muhammad Baid, Ujjwal Bergquist, Timothy Bhalerao, Radhika Chen, Sully Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Godfrey, Devon Hilal, Fathi Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kent, Collin Kirkpatrick, John Koer, Florian Van Leemput, Koen Li, Hongwei Bran Liu, Xinyang Mahtabfar, Aria McBurney-Lin, Shan McLean, Ryan Meier, Zeke Moawad, Ahmed W. Mongan, John Nedelec, Pierre Pajot, Maxence Piraud, Marie Rashid, Arif Reitman, Zachary Shinohara, Russell Takeshi Velichko, Yury Wang, Chunhao Warman, Pranav Wiggins, Walter Aboian, Mariam Albrecht, Jake Anazodo, Udunna Bakas, Spyridon Flanders, Adam Janas, Anastasia Khanna, Goldey Linguraru, Marius George Menze, Bjoern Nada, Ayman Rauschecker, Andreas M. Rudie, Jeff Tahon, Nourel Hoda Villanueva-Meyer, Javier Wiestler, Benedikt Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Lab Crestview Radiology Lagos Nigeria Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Missouri University ColumbiaMO United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Sage Bionetworks United States University of California San Francisco CA United States Duke University Medical Center School of Medicine United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM Central Institute for Translational Cancer Research Technical University of Munich Germany Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Helmholtz Ai Helmholtz Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Germany Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Neuroradiology Technical University of Munich Munich Germany University of Zurich Switzerland Department of Neurosurgery Thomas Jefferson Univer

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring;yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma. Copyright © 2023, The Authors. All rights reserved.

关键词： Tumors