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arXiv 2023年

作者： Lemos, Pablo Parker, Liam Hahn, ChangHoon Ho, Shirley Eickenberg, Michael Hou, Jiamin Massara, Elena Modi, Chirag Dizgah, Azadeh Moradinezhad Blancard, Bruno Régaldo-Saint Spergel, David Department of Physics Université de Montréal 1375 Avenue Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Mila - Quebec Artificial Intelligence Institute 6666 Rue Saint-Urbain MontréalQCH2S 3H1 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics Princeton University PrincetonNJ08544 United States Center for Cosmology and Particle Physics Department of Physics New York University New YorkNY10003 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States Center for Computational Mathematics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Astronomy University of Florida 211 Bryant Space Science Center GainesvilleFL32611 United States Max-Planck-Institut für Extraterrestrische Physik Postfach 1312 Giessenbachstrasse 1 Garching bei München85748 Germany Waterloo Centre for Astrophysics University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Département de Physique Théorique Université de Genève 24 quai Ernest Ansermet Genève 41211 Switzerland

We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing summary statistics, such as the power spectrum, P, with analytic models based on perturbation theory. Consequently, they do not fully exploit the non-linear and non-Gaussian features of the galaxy distribution. To address these limitations, we use the SimBIG forward modelling framework to perform SBI using normalizing flows. We apply SimBIG to a subset of the BOSS CMASS galaxy sample using a convolutional neural network with stochastic weight averaging to perform massive data compression of the galaxy field. We infer constraints on (Equation presented) and (Equation presented). While our constraints on Ωm are in-line with standard P analyses, those on σ8 are 2.65× tighter. Our analysis also provides constraints on the Hubble constant H0 = 64.5 ± 3.8 km/s/Mpc from galaxy clustering alone. This higher constraining power comes from additional non-Gaussian cosmological information, inaccessible with P. We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset. This work not only presents competitive cosmological constraints but also introduces novel methods for leveraging additional cosmological information in upcoming galaxy surveys like DESI, PFS, and Euclid. © 2023, CC0.

关键词： Galaxies

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PSXI-17 Evaluation of Influence of the Total Accumulation of Toxic Elements in Blood Serum on the Indicators of Milk Productivity of the Holstein Cows

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Journal of Animal Science 2022年第SUPPLEMENT_3期100卷 221–221页

作者： Zubac, Isidora Sycheva, Irina Komarchev, Alexey Nikitin, Petr Vasilev, Vladimir Vasilyeva, Irina Stepantsevich, Marina University of Belgrade Russian State Agrarian University–Moscow Timiryazev Agricultural Academy Federal Scientific Center «All-Russian Research and Technological Institute of Poultry» of Russian Academy of Sciences Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Regional Research and Clinical Institute "MONIKI" Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)

The main objective was to study the milk productivity of lactating cows depending on the level of total accumulation of toxic metals in the blood serum. The experimental part of work was carried out on clinically healthy Holstein cows (n=45; lactation stage - 20-40 days after calving) bred in a single farm. The elemental composition of blood serum was studied for 4 chemical elements (Al, Pb, Sn, Sr) by ICP AES and ICP MS. The indicators of milk productivity of cows were evaluated by the yield of milk fat, protein, lactose, dry matter, SOMO and the average daily milk yield. To assess the magnitude of the toxic load on the body of cows, the coefficient of the total toxic load Ktox (µg/ml) was calculated. To calculate the coefficient, the sum of the concentrations of some toxic elements in the blood serum was used: Ktox=Al+Pb+Sn+Sr. Based on the data on the value of the total toxic load coefficient Ktox in blood serum, three groups were formed: group I included cows with a minimum value of the toxic load coefficient (Ktox=0.128 (0.088-0.142) μg/ml relative to the study sample; group II with an average (Ktox=0.229 (0.206-0.276) µg/ml; Group III included cows with the maximum coefficient (Ktox=0.395 (0.306-0.453) µg/ml. Comparative analysis showed that cows with the minimum Ktox value (group I) outperformed individuals from I and group II in terms of milk fat yield by 15.4 (P£0.001) and 19.7% (P£0.001), protein by 5.6 and 12.5% (P£0.05), lactose -10.9 (P£0.01) and 18.4% (P£0.001), dry matter by -7.6 and 12.8% (P£0.05) and average daily milk yield by 9.4 and 17.5% (P£0.01), respectively. Thus, in the course of the study, it was registered that with an increase in the total accumulation of heavy metals in the body of animals, the milk productivity of cows decreases.

关键词： blood serum cattle milk productivity toxic elements

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Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

arXiv

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arXiv 2024年

作者： LaBella, Dominic Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia Velichko, Yury Wang, Chunhao Kirkpatrick, John Floyd, Scott Reitman, Zachary J. Mullikin, Trey Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Haider, Muhammad Ammar Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Coley, Aaron Huang, Christina Ghanta, Siddharth Schneider, Alex Sharp, Conrad Saluja, Rachit Kofler, Florian Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiation Oncology SUNY Upstate Medical University SyracuseNY United States San FranciscoCA United States Department of Neurosurgery King’s College Hospital London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Guy’s and St Thomas’ NHS Foundation Trust United Kingdom Department of Radiation Oncology University of Washington SeattleWA United States Department of Radiology Northwestern University EvanstonIL United States Department of Radiation Oncology Northwestern University EvanstonIL United States Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla CA United States Department of Radiology University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiation Oncology Indiana University IndianapolisIN United States Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States CMH Lahore Medical College Lahore Pakistan Duke University Medical Center School of Medicine DurhamNC United States Sage Bionetworks United States University of Zürich Zürich Switzerland Artificial Intelligence Lab Department of Radiology Mayo Clinic RochesterMN United States MLCommons United States Factored AI United States Center For Federated Learning in Medicine Indiana University IndianapolisIN United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States Intel United States Duke Universi

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. © 2024, CC BY.

关键词： Radiotherapy

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Strong gravitational lensing as a probe of dark matter

arXiv

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arXiv 2023年

作者： Vegetti, S. Birrer, S. Despali, G. Fassnacht, C.D. Gilman, D. Hezaveh, Y. Levasseur, L. Perreault McKean, J.P. Powell, D.M. O’Riordan, C.M. Vernardos, G. Max Planck Institut für Astrophysik Karl-Schwarzschild-Strasse 1 Garching MünchenD-85748 Germany Department of Physics and Astronomy Stony Brook University Stony BrookNY11794 United States Dipartimento di Fisica e Astronomia "A. Righi" Alma Mater Studiorum Università di Bologna Via Piero Gobetti 93/2 BolognaI-40129 Italy Institut für Theoretische Astrophysik Zentrum für Astronomie Heidelberg Universität Albert-Ueberle-Str. 2 Heidelberg69120 Germany Dept. of Physics and Astronomy University of California Davis 1 Shields Ave. DavisCA95616 United States Department of Astronomy and Astrophysics University of Toronto TorontoONM5S 3H4 Canada Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Kapteyn Astronomical Institute University of Groningen PO Box 800 AV Groningen9700 Netherlands ASTRON Netherlands Institute for Radio Astronomy PO Box 2 AA Dwingeloo7990 Netherlands Observatoire de Sauverny Versoix1290 Switzerland

Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution followup observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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Social stress drives the multi-wave dynamics of COVID-19 outbreaks

arXiv

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arXiv 2021年

作者： Kastalskiy, Innokentiy A. Pankratova, Evgeniya V. Mirkes, Evgeny M. Kazantsev, Victor B. Gorban, Alexander N. Department of Neurotechnology Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia 46 Ulyanov St. Nizhny Novgorod603950 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University 14 Nevsky St. Kaliningrad236016 Russia Laboratory of Perspective Methods for Analysis of Multidimensional Data Institute of Information Technology Mathematics and Mechanics Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia Department of Applied Mathematics Institute of Information Technology Mathematics and Mechanics Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia Department of Mathematics University of Leicester University Rd LeicesterLE1 7RH United Kingdom Neuroscience and Cognitive Technology Laboratory Innopolis University 1 Universitetskaya St. Innopolis420500 Russia Laboratory of Neuromodeling Samara State Medical University 18 Gagarin St. Samara443079 Russia

The dynamics of epidemics depend on how people's behavior changes during an outbreak. At the beginning of the epidemic, people do not know about the virus, then, after the outbreak of epidemics and alarm, they begin to comply with the restrictions and the spreading of epidemics may decline. Over time, some people get tired/frustrated by the restrictions and stop following them (exhaustion), especially if the number of new cases drops down. After resting for a while, they can follow the restrictions again. But during this pause the second wave can come and become even stronger then the first one. Studies based on SIR models do not predict the observed quick exit from the first wave of epidemics. Social dynamics should be considered. The appearance of the second wave also depends on social factors. Many generalizations of the SIR model have been developed that take into account the weakening of immunity over time, the evolution of the virus, vaccination and other medical and biological details. However, these more sophisticated models do not explain the apparent differences in outbreak profiles between countries with different intrinsic sociocultural features. In our work, a system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress with classical epidemic models. Social stress is described by the tools of sociophysics. The combination of a dynamic SIR-type model with the classical triad of stages of the general adaptation syndrome, alarm-resistance-exhaustion, makes it possible to describe with high accuracy the available statistical data for 13 countries. The sets of kinetic constants corresponding to optimal fit of model to data were found. These constants characterize the ability of society to mobilize efforts against epidemics and maintain this concentration over time, and can further help in the development of management strategies specific to a particular society. © 2021, CC BY.

关键词： Epidemiology

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Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report

arXiv

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arXiv 2024年

作者： Huijben, Evi M.C. Terpstra, Maarten L. Galapon, Arthur Pai, Suraj Thummerer, Adrian Koopmans, Peter Afonso, Manya van Eijnatten, Maureen Gurney-Champion, Oliver Chen, Zeli Zhang, Yiwen Zheng, Kaiyi Li, Chuanpu Pang, Haowen Ye, Chuyang Wang, Runqi Song, Tao Fan, Fuxin Qiu, Jingna Huang, Yixing Ha, Juhyung Park, Jong Sung Alain-Beaudoin, Alexandra Bériault, Silvain Yu, Pengxin Guo, Hongbin Huang, Zhanyao Li, Gengwan Zhang, Xueru Fan, Yubo Liu, Han Xin, Bowen Nicolson, Aaron Zhong, Lujia Deng, Zhiwei Müller-Franzes, Gustav Khader, Firas Li, Xia Zhang, Ye Hémon, Cédric Boussot, Valentin Zhang, Zhihao Wang, Long Bai, Lu Wang, Shaobin Mus, Derk Kooiman, Bram Sargeant, Chelsea A.H. Henderson, Edward G.A. Kondo, Satoshi Kasai, Satoshi Karimzadeh, Reza Ibragimov, Bulat Helfer, Thomas Dafflon, Jessica Chen, Zijie Wang, Enpei Perko, Zoltan Maspero, Matteo Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Radiotherapy Department University Medical Center Utrecht Utrecht Netherlands Computational Imaging Group for MR Diagnostics & Therapy University Medical Center Utrecht Utrecht Netherlands Department of Radiation Oncology University Medical Center Groningen University of Groningen Groningen Netherlands GROW School for Oncology Maastricht University Medical Centre Maastricht Netherlands Department of Radiation Oncology LMU University Hospital LMU Munich Munich Germany Department of Radiation Oncology Radboud University Medical Center Nijmegen Netherlands Wageningen University & Research Wageningen Plant Research Wageningen Netherlands Department of Radiology and Nuclear Medicine Amsterdam UMC Location University of Amsterdam Amsterdam Netherlands Cancer Center Amsterdam Imaging and Biomarkers Amsterdam Netherlands School of Biomedical Engineering Southern Medical University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China School of Biomedical Engineering ShanghaiTech University Shanghai China Fudan University Shanghai China Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Indiana University Bloomington United States Advanced Development Engineering Elekta Ltd Montreal Canada Infervision Medical Technology Co. Ltd. Beijing China Department of Biomedical Engineering Shantou University China Independent researchers Department of Computer Science Vanderbilt University Nashville United States Australian e-Health Research Centre CSIRO HerstonQLD Australia Los AngelesCA United States University Hospital Aachen Aachen Germany Center for Proton Therapy Paul Scherrer Institut Villigen Switzerland Department of Computer Science ETH Zurich Zurich Switzerland University Rennes 1 CLCC Eugène Marquis INSERM LTSI Rennes France Subtle Medical Shanghai China MedMind Technology Co.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥ 0.87/0.90) and gamma pass rates for photon (≥ 98.1%/99.0%) and proton (≥ 97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning. © 2024, CC

关键词： Radiotherapy

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Neural networks and their application in forecasting problems

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk F F Pashchenko Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

The report describes popular machine learning methods and applications of neural networks. It reveals methods of training neural networks and offers a method of forecasting based on neural networks for modelling financial time series. Neural networks have recently gained in popularity among scholars. In particular, neural networks are widely used in the field of visualization and image recognition. But the practical significance of neural networks does not end there, they also find usage in such areas as forecasting, classification, clustering and modelling. This success can mainly be attributed to the remarkable property of models based on neural networks – they can «see» non-linear connections in contrast to many models, which for the most part have linear connections only. Currently, the use of neural networks is developing in the following directions: Stock market and macroeconomic forecasting (Neuro XL, OptimuStock, StocksNeural); Speech recognition and man-machine dialogue (Siri, Alexa, Cortana, Alice); Imitation of intellectual activity (weak AI in Siri, Alexa, Cortana, Alice); Improving low-quality and noisy information (DeepImagePrior). The advantages of using neural networks include: versatility. simplicity. Neural networks are able to model dependencies also in cases when there is a large number of variables.

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Forecasting the dynamics of financial time series based on neural networks

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk N M Abdikeev A D Tsvirkun Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

Forecasting is one of the high-demand data mining problems, but also a very difficult one. The difficulties of forecasting are associated with insufficient quality and quantity of input data, the changes in the environment where the process takes place, and the impact of subjective factors. A forecast always implies some margin of error, which depends on the forecast model used and the completeness of the input data. Methods based on neural networks are the most relevant and highly-demanded techniques today. Neural networks are great for finding accurate solutions in an environment characterized by complex or fragmented information. In the field of finance and economics, the values of time series parameters can be more accurately modelled using neural analysis methods. Artificial neural networks have more common and flexible functional forms than statistical methods. They can generalize information and provide a qualitative forecast under conditions of uncertainty and crisis. The article proposes a forecasting model based on a neural network that can predict the price of a financial asset in a well-defined time interval. Ten technical indicators are used as input signals, and the closing price of the next period is used as an output signal.

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Ensemble forecasting method

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk A D Tsvirkun Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

The purpose of this article is to analyze the time series based on aggregate forecasting methods. Forecasting time series comprises an important scientific and technical task which is relevant in various sectors of economy and production. The main objective of any forecasting process is to get the ability to assess trends in changes to a particular factor. To predict the behaviour of future processes, a qualitative analysis of data is required, which provides the basis for the forecast. It is also important to choose the appropriate forecasting method. The forecast obtained may not be always correct, so it is crucial to determine how accurately it is built. In the present paper, characteristics of time series are provided. The main mathematical methods of time series forecasting are analyzed and their classification is presented. Criteria for the accuracy and reliability of forecasting models are defined. The accuracy of the forecast is an important criterion for evaluating the forecasting method. The aggregate ensemble forecast has proven to be an effective method for enhancing the accuracy of forecasting. The article provides an example of constructing an aggregate ensemble forecast. The types of nonlinear models are described and several methods are selected for constructing the aggregate ensemble forecast. The practical application of exponential smoothing is considered, the MA (moving average) model and the AR (autoregressive) model are constructed.

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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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