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Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]

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Medical image analysis 2025年 100卷 103363页

作者： Matthias Ivantsits Leonid Goubergrits Jan-Martin Kuhnigk Markus Huellebrand Jan Bruening Tabea Kossen Boris Pfahringer Jens Schaller Andreas Spuler Titus Kuehne Yizhuan Jia Xuesong Li Suprosanna Shit Bjoern Menze Ziyu Su Jun Ma Ziwei Nie Kartik Jain Yanfei Liu Yi Lin Anja Hennemuth Charité Universitätsmedizin Berlin Augustenburger Pl. 1 Berlin 13353 Germany. Electronic address: matthias.ivantsits@dhzc-charite.de. Charité Universitätsmedizin Berlin Augustenburger Pl. 1 Berlin 13353 Germany Einstein Center Digital Future Wilhelmstrae 67 Berlin 10117 Germany. Fraunhofer MEVIS Am Fallturm 1 Bremen 28359 Germany. Charité Universitätsmedizin Berlin Augustenburger Pl. 1 Berlin 13353 Germany. Helios Hospital Berlin-Buch Schwanebecker Chaussee 50 Berlin 13125 Germany. German Heart Centre Berlin Augustenburger Pl. 1 Berlin 13353 Germany DZHK (German Centre for Cardiovascular Research) Berlin Germany. Mediclouds Medical Technology Beijing China. School of Computer Science and Technology Beijing Institute of Technology Beijing China. Departments of Informatics Technical University Munich Germany TranslaTUM Center for Translational Cancer Research Munich Germany. TranslaTUM Center for Translational Cancer Research Munich Germany Department of Quantitative Biomedicine of UZH Zurich Switzerland. Department of Mathematics Nanjing University of Science and Technology Nanjing China. Department of Mathematics Nanjing University Nanjing China. Faculty of Engineering Technology University of Twente P.O. Box 217 Enschede 7500 AE the Netherlands. Jarvis Lab Tencent Shenzhen China Shenzhen United Imaging Research Institute of Innovative Medical Equipment Innovation Research Shenzhen China. Jarvis Lab Tencent Shenzhen China. Fraunhofer MEVIS Am Fallturm 1 Bremen 28359 Germany

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Designing ecosystems of intelligence from first principles

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Collective Intelligence 2024年第1期3卷

作者： Karl J Friston Maxwell JD Ramstead Alex B Kiefer Alexander Tschantz Christopher L Buckley Mahault Albarracin Riddhi J Pitliya Conor Heins Brennan Klein Beren Millidge Dalton AR Sakthivadivel Toby St Clere Smithe Magnus Koudahl Safae Essafi Tremblay Capm Petersen Kaiser Fung Jason G Fox Steven Swanson Dan Mapes Gabriel René VERSES Research Lab Los Angeles CA USA (KJF MJDR ABK AT CLB MA RJP CH BK BM DARS TSCS MK SET CP KF JGF SS DM GR) Wellcome Centre for Human Neuroimaging University College London London UK (KJF MJDR) Department of Philosophy Monash University Melbourne VIC Australia (ABK) Department of Informatics University of Sussex Brighton UK (CLB) Department of Computer Science Université du Québec à Montréal Montréal QC Canada (MA) Department of Experimental Psychology University of Oxford Oxford UK (RJP TSCS) Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz Germany (CH) Department of Biology University of Konstanz Konstanz Germany (CH) Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany (CH) Network Science Institute Northeastern University Boston MA USA (BK) Brain Network Dynamics Unit University of Oxford Oxford UK (BM) Department of Mathematics Stony Brook University New York NY USA (DARS) Department of Physics and Astronomy Stony Brook University New York NY USA (DARS) Department of Biomedical Engineering Stony Brook University New York NY USA (DARS) Topos Institute Berkeley CA USA (TSCS) Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands (MK) Department of Philosophy Université du Québec à Montréal Montréal QC Canada (SET)

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.

关键词： Active inference free energy principle artificial intelligence belief updating belief propagation

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Attack tolerance of link prediction algorithms: How to hide your relations in a social network

arXiv

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arXiv 2018年

作者： Waniek, Marcin Zhou, Kai Vorobeychik, Yevgeniy Moro, Esteban Michalak, Tomasz P. Rahwan, Talal Department of Computer Science Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Institute of Informatics University of Warsaw Warsaw Poland Computer Science and Engineering Washington University in Saint Louis Saint LouisMO United States Department of Mathematics and GISC Universidad Carlos III de Madrid Madrid Spain Media Lab Massachusetts Institute of Technology CambridgeMA United States

Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades. For all their benefits, such algorithms raise serious privacy concerns, as they could be used to expose a connection between two individuals who wish to keep their relationship private. With this in mind, we investigate the ability of such individuals to evade link prediction algorithms. More precisely, we study their ability to strategically alter their connections so as to increase the probability that some of their connections remain unidentified by link prediction algorithms. We formalize this question as an optimization problem, and prove that finding an optimal solution is NP-complete. Despite this hardness, we show that the situation is not bleak in practice. In particular, we propose two heuristics that can easily be applied by members of the general public on existing social media. We demonstrate the effectiveness of those heuristics on a wide variety of networks and against a plethora of link prediction *** Codes 91D30 (Primary) 68T20 (Secondary) Copyright © 2018, arXiv, All rights reserved.

关键词： Forecasting

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A large annotated medical image dataset for the development and evaluation of segmentation algorithms

arXiv

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arXiv 2019年

作者： Simpson, Amber L. Antonelli, Michela Bakas, Spyridon Bilello, Michel Farahani, Keyvan Van Ginneken, Bram Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc Golia-Pernicka, Jennifer Heckers, Stephan H. Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Vorontsov, Eugene Maier-Hein, Lena Jorge Cardoso, M. Department of Surgery Memorial Sloan Kettering Cancer Center Centre for Medical Image Computing University College London Center for Biomedical Image Computing and Analytics University of Pennsylvania Division of Cancer Treatment and Diagnosis National Cancer Institute Department of Pathology Radboud University Medical Center Division of Biostatistics German Cancer Research Center Department of Electrical Engineering and Computer Science Vanderbilt University Department of Informatics Technische Universität München Google DeepMind Imaging Biomarkers and Computer-aided Diagnosis Lab Radiology and Imaging Sciences National Institutes of Health Clinical Center Department of Radiology Memorial Sloan Kettering Cancer Center Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center Department of Radiology Stanford University Department of Computer Science and Software Engineering École Polytechnique de Montréal Division of Computer Assisted Medical Interventions German Cancer Research Center Department of Imaging and Biomedical Engineering King's College London

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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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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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 — Ex vivo imaging: added value and acquisition

arXiv

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arXiv 2022年

作者： Schilling, Kurt G. Grussu, Francesco Ianus, Andrada Hansen, Brian Howard, Amy F.D. Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Allan Johnson, G. Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Re

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio (SNR) and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY.

关键词： Image acquisition

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Prediction of soft proton intensities in the near-Earth space using machine learning

arXiv

引用

arXiv 2021年

作者： Kronberg, Elena A. Hannan, Tanveer Huthmacher, Jens Münzer, Marcus Peste, Florian Zhou, Ziyang Berrendorf, Max Faerman, Evgeniy Gastaldello, Fabio Ghizzardi, Simona Escoubet, Philippe Haaland, Stein Smirnov, Artem Sivadas, Nithin Allen, Robert C. Tiengo, Andrea Ilie, Raluca Munich Theresienstr. 41 Munich80333 Germany Institute of Informatics LMU Munich Oettingenstraße 67 MunichD-80538 Germany Department of Statistics LMU Munich Ludwigstraße 33 MunichD-80539 Germany Milano via A. Corti 12 MilanoI-20133 Italy European Space Research and Technology Centre Keplerlaan 1 Noordwjik2201 AZ Netherlands Birkeland Centre for Space Science University of Bergen Allégaten 55 BergenNO-5007 Norway Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany German Research Centre for Geosciences Albert-Einstein-Straße 42-46 PotsdamD-14473 Germany Department of Electrical and Computer Engineering Boston University 8 Saint Mary's Street BostonMA02134 United States Johns Hopkins University Applied Physics Lab 11100 Johns Hopkins Road LaurelMD20723 United States Scuola Universitaria Superiore IUSS Pavia piazza della Vittoria 15 PaviaI-27100 Italy INFN Sezione di Pavia via A. Bassi 6 PaviaI-27100 Italy University of Illinois at Urbana-Champaign ECEB 306 N. Wright Street 5054 UrbanaIL61801 United States

The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have derived machine learning-based models to predict the proton intensities at energies from 28 to 1,885 keV in the 3D terrestrial magnetosphere at radial distances between 6 and 22 RE. We used the satellite location and indices for solar, solar wind and geomagnetic activity as predictors. The results demonstrate that the neural network (multi-layer perceptron regressor) outperforms baseline models based on the k-Nearest Neighbors and historical binning on average by ∼80% and ∼33%, respectively. The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere. In addition to a quantitative analysis of the prediction results, we also investigate parameter importance in our model. The most decisive parameters for predicting proton intensities are related to the location: ZGSE direction and the radial distance. Among the activity indices, the solar wind dynamic pressure is the most important. The results have a direct practical application, for instance, for assessing the contamination particle background in the X-Ray telescopes for X-ray astronomy orbiting above the radiation belts. To foster reproducible research and to enable the community to build upon our work we publish our complete code, the data, as well as weights of trained models. Further description can be found in the GitHub project at https://***/Tanveer81/deep horizon. © 2021, CC BY-NC-ND.

关键词： Forecasting

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

引用

arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian labella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC 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 Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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