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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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Disentangling Alzheimer’s disease neurodegeneration from typical brain aging using MRI and machine learning

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Alzheimer's & Dementia 2022年第S4期17卷

作者： Gyujoon Hwang Ahmed Abdulkadir Guray Erus Mohamad Habes Raymond Pomponio Haochang Shou Jimit Doshi Elizabeth Mamourian Tanweer Rashid Murat Bilgel Yong Fan Aristeidis Sotiras Dhivya Srinivasan John C. Morris Daniel S. Marcus Marilyn S. Albert Nick Bryan Susan M. Resnick Ilya M. Nasrallah Christos Davatzikos David A. Wolk Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio TX USA Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Washington University in St. Louis St. Louis MO USA Washington University in St Louis St Louis MO USA Johns Hopkins University School of Medicine Baltimore MD USA University of Texas at Austin Austin TX USA Penn Memory Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Neuroimaging biomarkers that discriminate between healthy brain aging (BA) and Alzheimer’s disease (AD) are valuable in assessing the heterogeneity of neurodegeneration. Prior work has demonstrated that machine learning can detect patterns of brain change related to the two processes on an individual level (including the SPARE [Spatial Patterns of Atrophy for REcognition]-AD and SPARE-BA indices investigated herein). However, the substantial overlap between brain regions affected in AD and BA confounds our ability to measure them independently. We present a methodology, and associated results, toward disentangling the two. Method T1-weighted MR images of 4,078 participants (48-95 years) with AD, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. First, a subset of 711 patients with probable AD and 711 age- and sex-matched probable CN adults were selected based purely on clinical diagnosis to train multivariate models of brain age (SPARE-BA1; regression of age using CN) and of an AD index (SPARE-AD1; classification of CN versus AD). Second, analogous groups a+AD and a-CN were selected based on clinical and molecular markers (a+AD: 711 amyloid-positive subjects with AD/MCI, as well as amyloid- and tau-positive CN adults; a-CN: 711 amyloid-negative CN adults). Finally, the combined group of a-CN and a+AD was used to train SPARE-BA3 model, with the notion that it would avoid AD-related changes. Result Several subcortical brain regions became more weighted in the SPARE-BA3 model than in SPARE-BA1 and SPARE-BA2, while regions in the temporal and occipital lobes became more weighted in SPARE-AD2. The correlation between the brain age gap (SPARE-BA minus chronological age) and SPARE-AD in the a-CN group was reduced to insignificant levels ( r =0.29 to -0.06). While both SPARE-AD scores well separated AD from CN (area-under-the-

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Exploration of Differentiability in a Proton Computed Tomography Simulation Framework

arXiv

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arXiv 2022年

作者： Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Blühdorn, Johannes Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Eikeland, Viljar Feofilov, Gregory Garth, Christoph Gauger, Nicolas R. Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Kortus, Tobias Kusch, Lisa Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Pochampalli, Rohit Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Sagebaum, Max Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Zillien, Sebastian Chair for Scientific Computing University of Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab University of Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway University of Applied Sciences Worms Worms Germany Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary FSN Department ENEA Frascati Research Center Frascati00044 Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how "well-linearizable" the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path. Jumps in the MC function likely arose from changes in the control flow that affect the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. The MC and MBIR codes are ready for the integration of AD, and further research on surrogate models for the tracking subprocedure is necessary. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Bayesian inference for gravitational waves from binary neutron star mergers in third-generation observatories

arXiv

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arXiv 2021年

作者： Smith, Rory Borhanian, Ssohrab Sathyaprakash, Bangalore Vivanco, FranciscoHernandez Field, Scott E. Lasky, Paul Mandel, Ilya Morisaki, Soichiro Ottaway, David Slagmolen, Bram Thrane, Eric Töyrä, Daniel Vitale, Salvatore School of Physics and Astronomy Monash University VIC3800 Australia OzGrav The ARC Centre of Excellence for Gravitational Wave Discovery ClaytonVIC3800 Australia Institute for Gravitation and the Cosmos Department of Physics Pennsylvania State University University ParkPA16802 United States Department of Astronomy and Astrophysics Penn State University University ParkPA16802 United States School of Physics and Astronomy Cardiff University CardiffCF24 3AA United Kingdom Department of Mathematics Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States OzGrav University of Adelaide AdelaideSA5005 Australia OzGrav ANU Centre for Gravitational Astrophysics Research Schools of Physics and Astronomy and Astrophysics The Australian National University ACT2601 Australia LIGO Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States

Third-generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced order models for ∼ 90 minute long gravitational-wave signals, covering the observing band (5−2048 Hz), speeding up inference by a factor of ∼ 1.3 × 104 compared to the calculation times without reduced order models. The reduced order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to the Earth's rotation, and spin-induced orbital precession. We show how reduced order modeling can accelerate inference on data containing multiple, overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories. Copyright © 2021, The Authors. All rights reserved.

关键词： Gravitational effects

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Genetic heterogeneity of four MCI/AD neuroanatomical dimensions discovered via deep learning

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Alzheimer's & Dementia 2022年第S6期18卷

作者： Junhao Wen Yuhan Cui Zhijian Yang Jingxuan Bao Jiong Chen Guray Erus Ahmed Abdulkadir Elizabeth Mamourian Ashish Singh Shu Yang Yong Fan Andrew J. Saykin Paul M Thompson Gyungah R Jun Marylyn D. Ritchie Li Shen David A. Wolk Haochang Shou Ilya M. Nasrallah Christos Davatzikos Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Upenn Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA University of Bern Bern Switzerland Upenn Phialdelphia PA USA Indiana University Indianapolis IN USA University of Southern California Marina del Rey CA USA Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del Rey CA USA Boston University School of Public Health Boston MA USA Boston University School of Medicine Boston MA USA Eisai Andover Innovative Medicines (AiM) Institute Andover MA USA University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Indiana University School of Informatics and Computing Indianapolis IN USA Indiana University School of Medicine Indianapolis IN USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Alzheimer’s Disease Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Data-driven unsupervised and semi-supervised clustering methods have parsed neuroanatomical heterogeneity of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) into multidimensional neuroimaging representations (Vogel et al., 2021; Wen et al., 2021b, 2021a; Yang et al., 2021; Young et al., 2018; Zhang et al., 2016). However, whether this neuroanatomical heterogeneity is partly underpinned by genetic heterogeneity remains unanswered. Method Whole-genome sequencing (WGS) data were analyzed for individuals in the Alzheimer’s Disease Neuroimaging Initiative. WGS data underwent a standard genetic pipeline. This resulted in 1,487 participants (428 healthy controls, 489 MCI and 570 AD; age = 78.96 ± 7.72; 44.05% females) with 24,773,167 single nucleotide polymorphism (SNP). The deep learning Smile-GAN model (Yang et al., 2021) generates expression scores (ES) across each of the four subtypes of MCI/AD ( Fig. 2 ). The ES of each subtype were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for age, sex, intracranial volume, disease diagnosis, and the first four genetic principle components using Plink (Purcell et al., 2007). We then performed clumping to define the independent significant variants (ISVs). Result GWAS discovered 17 ISV-ES pairwise associations. WGS data allowed us to identify four de no ISVs that were not included in dbSNP ( Fig. 1 ). P1 and P4 detected similar AD-related genetic variants (e.g., APOE, NECTIN2, and TOMM40, Fig. 1 and 2 ), but with opposite directions of effect, i.e., protective factors for P1 and risk factors for P4, consistent with respective patterns of neurodegeneration in these phenotypes. In particular, for rs429358 (APOE-4), the P4 subtype had more C alleles (minor allele) than the P1 subtype (P-value < 1e-20). The four ISVs identified for P2 and P3 were not previously associated with any clinical trait. Conclusion The current study i

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Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning

TechRxiv

引用

TechRxiv 2022年

作者： Kortus, Tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Genov, Georgi Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Sølie, Jarle Rambo Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Yokoyama, Hiroki Zillien, Sebastian University of Applied Sciences Worms Worms67549 Germany Chair for Scientific Computing TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking. © 2022, CC BY.

关键词： Reinforcement learning

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Attention-guided sampling for colorectal cancer analysis with digital pathology

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Journal of Pathology Informatics 2022年 13卷 100110-100110页

作者： Broad, Andrew Wright, Alexander I. de Kamps, Marc Treanor, Darren School of Computing University of Leeds Sir William Henry Bragg Building Woodhouse Lane UK Leeds LS2 9BW United Kingdom Leeds Institute for Data Analytics University of Leeds Level 11 Worsley Building Clarendon Way UK Leeds LS2 9NL United Kingdom Leeds Teaching Hospitals NHS Trust Beckett St Harehills UK Leeds LS9 7TF United Kingdom Division of Pathology and Data Analytics Leeds Institute of Medical Research University of Leeds St James's University Hospital UK Leeds LS9 7TF United Kingdom The Alan Turing Institute 96 Euston Road UK London NW1 2DB United Kingdom University of Leeds UK Leeds LS2 9JT United Kingdom Department of Clinical Pathology Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas. We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging. These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour–stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists’ annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing. We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations. © 2022 The Authors

关键词： Artificial intelligence Attention Colorectal cancer Region of interest Sampling Tumour–stroma ratio

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Correction: Characterizing the neurological phenotype of the hyperinsulinism hyperammonemia syndrome

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Orphanet journal of rare diseases 2022年第1期17卷 315页

作者： Elizabeth Rosenfeld Ravi Prakash Reddy Nanga Alfredo Lucas Andrew Y Revell Allison Thomas Nina H Thomas David R Roalf Russell T Shinohara Ravinder Reddy Kathryn A Davis Diva D De León Division of Endocrinology and Diabetes Children's Hospital of Philadelphia 3500 Civic Center Boulevard Philadelphia PA 19140 USA. rosenfelde@chop.edu. Congenital Hyperinsulinism Center Children's Hospital of Philadelphia Philadelphia PA USA. rosenfelde@chop.edu. Department of Pediatrics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. rosenfelde@chop.edu. Center for Advanced Metabolic Imaging in Precision Medicine Hospital of the University of Pennsylvania Philadelphia PA USA. Penn Center for Neuroengineering and Therapeutics University of Pennsylvania Philadelphia PA USA. Behavioral Neuroscience Core Center for Human Phenomic Science Children's Hospital of Philadelphia Philadelphia PA USA. Department of Child and Adolescent Psychiatry and Behavioral Sciences Children's Hospital of Philadelphia Philadelphia PA USA. Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Center for Biomedical Image Computing and Analytics Department of Radiology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Division of Endocrinology and Diabetes Children's Hospital of Philadelphia 3500 Civic Center Boulevard Philadelphia PA 19140 USA. Congenital Hyperinsulinism Center Children's Hospital of Philadelphia Philadelphia PA USA. Department of Pediatrics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA.

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Machine-learning based MRI neuro-anatomical signatures associated with cardiovascular and metabolic risk factors

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Alzheimer's & Dementia 2022年第S1期18卷

作者： Sindhuja Tirumalai Govindarajan Elizabeth Mamourian Guray Erus Ahmed Abdulkadir Randa Melhem Jimit Doshi Raymond Pomponio Duygu Tosun Yang An Aristeidis Sotiras Daniel S. Marcus Pamela J. LaMontagne Mark A. Espeland Colin L. Masters Paul Maruff Lenore J. Launer Jurgen Fripp Sterling C. Johnson John C. Morris Marilyn S. Albert R. Nick Bryan Mohamad Habes Haochang Shou David A. Wolk Ilya M. Nasrallah Christos Davatzikos Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of California San Francisco San Francisco CA USA Laboratory of Behavioral Neuroscience National Institute on Aging Intramural Research Program Baltimore MD USA Washington University in St. Louis School of Medicine St. Louis MO USA Wake Forest School of Medicine Winston-Salem NC USA Florey Institute of Neuroscience and Mental Health Parkville VIC Australia The Florey Institute of Neuroscience and Mental Health Melbourne VIC Australia Laboratory of Epidemiology and Population Sciences National Institute on Aging Baltimore MD USA CSIRO Health and Biosecurity Australian E-Health Research Centre Brisbane QLD Australia Wisconsin Alzheimer's Disease Research Center University of Wisconsin School of Medicine and Public Health Madison WI USA Knight Alzheimer Disease Research Center St. Louis MO USA Department of Neurology Division of Cognitive Neuroscience John’s Hopkins University School of Medicine Baltimore MD USA University of Pennsylvania Philadephia PA USA Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases University of Texas Health Sciences Center San Antonio TX USA Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Background Lower gray matter (GM) and higher white matter (WM) volumes have been observed in cognitively unimpaired people with cardiovascular and metabolic risk factors (CVMs) such as hypertension, hyperlipidemia, diabetes, obesity, and smoking [Erus 2015, Habes 2016]. However, these group-level observations do not explain the heterogeneous accumulated effects of CVMs on individual neuroanatomy. Multivariate CVM-related changes characterized at the individual level using machine learning (ML)-derived summary indices may better capture their differential roles in brain aging and dementia. In this work, we derive ML-based indices that summarize brain changes from MRI related to CVMs. Method Cognitively normal volunteers that were pooled and harmonized for the iSTAGING cohort were included in this study (N=24,902 from 8 independent studies, 54.5% female, average age=62.4, age range 45-75 years). Linear support vector classifiers were trained to discriminate between people with a specific CVM from those without the CVM using nested cross-validation for hyperparameter tuning and model selection. Input features (n=150) consisted of age, sex, intracranial volume, harmonized regional GM and WM volumes, and lobar volumes of WM hyperintensities. The resulting distance of the projection onto the vector perpendicular to the decision plane yields a continuous scalar representing ‘spatial pattern of abnormality for recognition’ (SPARE) of each CVM. SPARE-CVMs were computed for each participant and correlated with previously established imaging markers for brain age [Habes 2021] and Alzheimer’s disease (SPARE-AD)[Davatzikos 2009]. Effect sizes (Cohen’s d) were calculated to assess the utility of SPAREs with higher values indicating greater separability between the CVM+ and CVM- groups. Result Figure 1 shows the distribution, effect sizes and associated brain regions for each SPARE-CVM. SPARE-diabetes and SPARE-hypertension yielded the largest effect sizes. SPAREs for cooccurring

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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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