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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases

arXiv

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arXiv 2020年

作者： Wen, Junhao Varol, Erdem Chand, Ganesh Sotiras, Aristeidis Davatzikos, Christos Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia United States Department of Statistics Center for Theoretical Neuroscience Zuckerman Institute Columbia University New York United States Department of Radiology School of Medicine Washington University in St. Louis St. Louis United States Department of Radiology Institute for Informatics Washington University School of Medicine St. Louis United States

There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimer’s Disease (AD). Elucidating distinct subtypes of diseases allows a better understanding of neuropathogenesis and enables the possibility of developing targeted treatment programs. Recent semi-supervised clustering techniques have provided a data-driven way to understand disease heterogeneity. However, existing methods do not take into account that subtypes of the disease might present themselves at different spatial scales across the brain. Here, we introduce a novel method, MAGIC, to uncover disease heterogeneity by leveraging multi-scale clustering. We first extract multi-scale patterns of structural covariance (PSCs) followed by a semi-supervised clustering with double cyclic block-wise optimization across different scales of PSCs. We validate MAGIC using simulated heterogeneous neuroanatomical data and demonstrate its clinical potential by exploring the heterogeneity of AD using T1 MRI scans of 228 cognitively normal (CN) and 191 patients. Our results indicate two main subtypes of AD with distinct atrophy patterns that consist of both fine-scale atrophy in the hippocampus as well as large-scale atrophy in cortical regions. The evidence for the heterogeneity is further corroborated by the clinical evaluation of two subtypes, which indicates that there is a subpopulation of AD patients that tend to be younger and decline faster in cognitive performance relative to the other subpopulation, which tends to be older and maintains a relatively steady decline in cognitive abilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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NIMG-82. RADIOGENOMIC SIGNATURES OF KEY DRIVER GENES IN GBM REVEAL MOLECULAR HETEROGENEITY OF THE TUMOR MICROENVIRONMENT LINKED TO SPATIAL DISTRIBUTION: IMPACT ON THE TRAJECTORY OF GLIOMA EVOLUTION

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Neuro-Oncology 2022年第SUPPLEMENT_7期24卷 vii184–vii184页

作者： Kazerooni, Anahita Fathi Akbari, Hamed Toorens, Erik Grigoriadis, Dimitris Hu, Xiaoju Sako, Chiharu Mamourian, Elizabeth Bagley, Stephen Binder, Zev A Lustig, Robert Brem, Steven O'Rourke, Donald Ganguly, Tapan De, Subhajyoti Hatzigeorgiou, Artemis Bakas, Spyridon Nasrallah, MacLean Davatzikos, Christos Center for Biomedical Image Computing and Analytics and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia USA University of Thessaly Lamia Greece Rutgers Cancer Institute of New Jersey and Rutgers Robert Wood Johnson Medical School New Brunswick NJ USA Hospital of the University of Pennsylvania Philadelphia PA USA Hospital of the University of Pennsylvania Philadelphia USA University of Pennsylvania Phildelphia USA Department of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Center for Biomedical Image Computing and Analytics Department of Radiology and Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

Somatic genomic alterations acquired during GBM growth enhance adaptation of tumor cells to their microenvironment and give rise to molecular heterogeneity. Radiogenomics could facilitate exploration of the underlying pathobiology of tumor growth in specific microenvironments and thereby, promote precision medicine for the patients. We derived radiogenomic signatures of key driver genes and evaluated molecular compositions of tumor groups with predisposition to specific brain regions. Pre-operative multiparametric conventional MRI scans of 357IDH-wildtype GBM patients with available targeted NGS data were jointly segmented and registered into a common template. We constructed spatial distribution atlases for tumors harboring mutations in driver genes and identified four distinct groups of tumor locations with predilection to the left frontal cingulate region (Group1), right temporal (Group2), right parietal (Group3), and occipital pole (Group4). Evaluation of the differences in molecular features of the tumor groups included: (1) exploring similarities of genomic profiles across all four groups by evaluating cosine similarity metric (CSM) between mutational signatures; (2) quantification of molecular heterogeneity based on Mutant Allele Tumor Heterogeneity (MATH) scores; and (3) inference of the evolutionary trajectories. Groups 1 and 4 were the most different, and Groups 2 and 3 were the most similar tumors, molecularly. The mutational signatures between Groups 1 and 4 revealed a CSM of 0.35. Group1 showed significantly lower MATH score (less heterogeneity) compared to Group4 (p< 0.05). Evaluation of evolutionary patterns suggestedNF1mutation as an early event in Group1, without subsequent gain of function or mutation inEGFR. In contrast, in Group4,EGFRmutations were early events triggeringPTENmutations later in the evolutionary trajectory. Radiogenomic signatures revealed distinct molecular underpinnings for the tumors with predilection towards specific brain regi

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Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

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Computers in Biology and Medicine 2024年 176卷 108588-108588页

作者： Gómez-Pascual, Alicia Naccache, Talel Xu, Jin Hooshmand, Kourosh Wretlind, Asger Gabrielli, Martina Lombardo, Marta Tiffany Shi, Liu Buckley, Noel J. Tijms, Betty M. Vos, Stephanie J.B. ten Kate, Mara Engelborghs, Sebastiaan Sleegers, Kristel Frisoni, Giovanni B. Wallin, Anders Lleó, Alberto Popp, Julius Martinez-Lage, Pablo Streffer, Johannes Barkhof, Frederik Zetterberg, Henrik Visser, Pieter Jelle Lovestone, Simon Bertram, Lars Nevado-Holgado, Alejo J. Gualerzi, Alice Picciolini, Silvia Proitsi, Petroula Verderio, Claudia Botía, Juan A. Legido-Quigley, Cristina Department of Information and Communications Engineering Faculty of Informatics University of Murcia Murcia Spain Steno Diabetes Center Copenhagen Herlev Denmark Department of Data Science City University of London United Kingdom Institute of Pharmaceutical Science King's College London London United Kingdom CNR Institute of Neuroscience Vedano al Lambro20854 Italy School of Medicine and Surgery University of Milano-Bicocca 20126 Italy Oxford United Kingdom Department of Psychiatry University of Oxford United Kingdom Kavli Institute for Nanoscience Discovery Denmark Alzheimer Center VU University Medical Center Amsterdam Netherlands Department of Psychiatry and Neuropsychology Alzheimer Centrum Limburg Maastricht University Maastricht Netherlands Department of Biomedical Sciences University of Antwerp Antwerp Belgium Vrije Universiteit Brussel Brussels Belgium Complex Genetics Group VIB Center for Molecular Neurology VIB Antwerp Belgium Institute Born-Bunge Department of Biomedical Sciences University of Antwerp Antwerp Belgium University of Geneva Geneva Switzerland IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Institute of Neuroscience and Physiology Sahlgrenska Academy at University of Gothenburg Gothenburg Sweden Barcelona Spain Old age psychiatry University Hospital of Lausanne University of Lausanne Switzerland Department of Geriatric Psychiatry University Hospital of Psychiatry Zürich University of Zürich Switzerland CITA-Alzheimer Foundation San Sebastian Spain AC Immune SA formerly Janssen R&D LLC. Beerse Belgium at the time of study conduct Lausanne Switzerland Department of Radiology and Nuclear Medicine Amsterdam UMC Vrije Universiteit Netherlands Queen Square Institute of Neurology and Centre for Medical Image Computing University College London United Kingdom Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Mölndal Sweden Department of Psychiatry and Neurochemistry Institute of

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways. © 2024 The Authors

关键词： Metabolites

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

arXiv

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arXiv 2021年

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

Neuroimaging biomarkers that distinguish between changes due to typical brain aging and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multi-variate patterns of brain change related to the two processes (including the SPARE-AD [Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease] and SPARE-BA [of Brain Aging] scores investigated herein). However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, toward disentangling the two. T1-weighted MRI images of 4,054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment, or cognitively normal diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically- or molecularly-defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched cognitively normal adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using cognitively normal individuals) and SPARE-AD1 (classification of cognitively normal versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718;consisting of amyloid-positive Alzheimer’s disease, amyloid-positive mild cognitive impairment, and amyloid- and tau-positive cognitively normal individuals) and amyloid-negative cognitively normal group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative cognitively normal individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regar

关键词： Magnetic resonance imaging

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GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows in Medical Imaging

arXiv

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arXiv 2021年

作者： Pati, Sarthak Thakur, Siddhesh P. Hamamcı, İbrahim Ethem Baid, Ujjwal Baheti, Bhakti Bhalerao, Megh Güley, Orhun Mouchtaris, Sofia Lang, David Thermos, Spyridon Gotkowski, Karol González, Camila Grenko, Caleb Getka, Alexander Edwards, Brandon Sheller, Micah Wu, Junwen Karkada, Deepthi Panchumarthy, Ravi Ahluwalia, Vinayak Zou, Chunrui Bashyam, Vishnu Li, Yuemeng Haghighi, Babak Chitalia, Rhea Abousamra, Shahira Kurc, Tahsin M. Gastounioti, Aimilia Er, Sezgin Bergman, Mark Saltz, Joel H. Fan, Yong Shah, Prashant Mukhopadhyay, Anirban Tsaftaris, Sotirios A. Menze, Bjoern Davatzikos, Christos Kontos, Despina Karargyris, Alexandros Umeton, Renato Mattson, Peter Bakas, Spyridon MLCommons Medical Working Group San FranciscoCA United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany International School of Medicine Istanbul Medipol University Marmara Istanbul Turkey Department of Bioengineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Mathematics School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Institute for Digital Communications School of Engineering The University of Edinburgh Scotland United Kingdom Department of Computer Science Technical University of Darmstadt Hesse Darmstadt Germany Intel Corporation Santa ClaraCA United States Department of Computer Science Stony Brook University Stony Brook New YorkNY United States Department of Biomedical Informatics Stony Brook University Stony Brook New YorkNY United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Institute of Image-Guided Surgery of Strasbourg Strasbourg France Department of Informatics & Analytics Dana-Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine Weill Cornell Medicine New YorkNY United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Department of Biological Engineering Department of Mechanical Engineering Massachusetts Institute of Technology BostonMA United States Google Menlo ParkCA United States

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows. © 2021, CC BY.

关键词： Medical imaging

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Multidimensional representations in late-life depression: Convergence in neuroimaging, cognition, clinical symptomatology and genetics

arXiv

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arXiv 2021年

作者： Wen, Junhao Fu, Cynthia H.Y. Tosun, Duygu Veturi, Yogasudha Yang, Zhijian Abdulkadir, Ahmed Mamourian, Elizabeth Srinivasan, Dhivya Bao, Jingxuan Erus, Guray Shou, Haochang Habes, Mohamad Doshi, Jimit Varol, Erdem Mackin, Scott R. Sotiras, Aristeidis Fan, Yong Saykin, Andrew J. Sheline, Yvette I. Shen, Li Ritchie, Marylyn D. Wolk, David A. Albert, Marilyn Resnick, Susan M. Davatzikos, Christos Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia United States University of East London School of Psychology London United Kingdom Centre for Affective Disorders Institute of Psychiatry Psychology and Neuroscience King’s College London London United Kingdom Department of Radiology and Biomedical Imaging University of California San FranciscoCA United States Department of Genetics Institute for Biomedical Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine PhiladelphiaPA19104 United States Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia United States Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio United States Department of Statistics Center for Theoretical Neuroscience Zuckerman Institute Columbia University New York United States Department of Psychiatry University of California San FranciscoCA United States Department of Radiology Institute for Informatics Washington University School of Medicine St. Louis United States Radiology and Imaging Sciences Center for Neuroimaging Department of Radiology and Imaging Sciences Indiana Alzheimer’s Disease Research Center Melvin and Bren Simon Cancer Center Indiana University School of Medicine Indianapolis United States Center for Neuromodulation in Depression and Stress Department of Psychiatry University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Department of Neurology Penn Memory Center University of Pennsylvania Philadelphia United States Department of Neurology Johns Hopkins University School of Medicine United States Laboratory o

Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to precision and individualized medicine. Objective: We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles. Design & setting: The iSTAGING study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with over 35,000 participants, including a subset of individuals with major depressive disorders. Participants: Multimodal data from a multicentre sample (N=996), including neuroimaging, neurocognitive assessments, and genetics: 501 LLD participants (332 women, mean age 67.39 ± 5.56 years) and 495 healthy controls (333 women, mean age 66.53 ± 5.16 years) were analyzed. A semi-supervised clustering method (HYDRA) was applied to regional grey matter (GM) brain volumes to derive dimensional representations. Exposure: None Main outcome and Measure: Two dimensions were identified, which accounted for the LLD-related heterogeneity in voxel-wise GM maps, white matter (WM) fractional anisotropy (FA), neurocognitive functioning, clinical phenotype, and genetics. Results: Dimension one (Dim1) demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy controls. In contrast, dimension two (Dim2) showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, one de novo independent genetic variant (rs13120336) was significantly associated with Dim 1 but not with Dim 2. Notably, the two dimensions demonstrated significant SNP-based heritability of 18-27% within the general population (N=12,518 in UKBB). Lastly, in a subset of individuals having longitudinal measurements, Dim2 demon

关键词： Neuroimaging

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Cross-scale functional connectivity patterns of the aging brain learned from the multi-cohort iSTAGing study

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Zhen Zhou Hongming Li Chau B Tran Yuncong Ma Dhivya Srinivasan Ahmed Abdulkadir Junhao Wen Guray Erus Elizabeth Mamourian Ilya M. Nasrallah Nick Bryan David A. Wolk Lori L Beason-Held Susan M. Resnick Haochang Shou Christos Davatzikos Yong Fan University of Pennsylvania philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Texas at Austin Austin TX USA Department of Pathology and Laboratory Medicine Alzheimer’s Disease Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA National Institute on Aging Baltimore MD USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA Correspondece

Background Brain functional connectivity (FC) measures derived from resting-state fMRI (rsfMRI) data have advanced our understanding of the brain organization. However, most existing studies investigate the brain functional networks (FNs) at a specific scale, ignoring potentially complementary FC information at different levels of spatial granularity as well as the cross-scale information. Given that the brain is a multi-scale, hierarchical system, FCs between FNs at different spatial scales may provide complementary information to those measured within-scales for characterizing the aging brain. Method We computed multiscale FNs to capture their FC measures across multiple scales and investigated the associations between cross-scale FCs and the brain aging based on rsfMRI data of a diverse cohort (n = 4186, aged 22 to 97 years) from seven sites of the iSTAGING study [1]. For each subject, we computed the FNs at seven scales, yielding seven sets of k (k = 17, 25, 50, 75, 100, 125 and 150) FNs, same as our previous study [2-3]. We estimated FC measures between the 542 FNs using Pearson’s correlation, yielding the cross-scale FC matrices that were harmonized across sites in the tangent space using ComBat-GAM [4] with sex and age as covariates. The harmonized FC measures were used as features to build an age prediction model using Ridge regression as illustrated in Figure 1. The brain age prediction model was compared with that built on harmonized FC measures between FNs of the same scales, with the performance estimated with a nested five-fold cross-validation. Result The cross-scale FC measures performed significantly better than the within-scale FC measures for the brain age prediction, as summarized in Table 1. Figure 2 shows the most informative FC measures for the brain age prediction. Specifically, FC measures computed both cross- and within-scales between the visual, somatomotor, and frontoparietal networks provided relatively more useful information for the bra

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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

arXiv

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arXiv 2023年

作者： Graham, Simon Vu, Quoc Dang Jahanifar, Mostafa Weigert, Martin Schmidt, Uwe Zhang, Wenhua Zhang, Jun Yang, Sen Xiang, Jinxi Wang, Xiyue Rumberger, Josef Lorenz Baumann, Elias Hirsch, Peter Liu, Lihao Hong, Chenyang Aviles-Rivero, Angelica I. Jain, Ayushi Ahn, Heeyoung Hong, Yiyu Azzuni, Hussam Xu, Min Yaqub, Mohammad Blache, Marie-Claire Piégu, Benoît Vernay, Bertrand Scherr, Tim Böhland, Moritz Löffler, Katharina Li, Jiachen Ying, Weiqin Wang, Chixin Snead, David Raza, Shan E. Ahmed Minhas, Fayyaz Rajpoot, Nasir M. Tissue Image Analytics Centre University of Warwick Coventry United Kingdom Histofy Ltd Birmingham United Kingdom Institute of Bioengineering School of Life Sciences Epfl Lausanne Switzerland Dresden Germany The Department of Computer Science The University of Hong Kong Hong Kong Tencent Ai Lab Shenzhen China College of Biomedical Engineering Sichuan University Chengdu China Department of Precision Instruments Tsinghua University Beijing China College of Computer Science Sichuan University Chengdu China Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association Berlin Germany Humboldt University of Berlin Faculty of Mathematics and Natural Sciences Berlin Germany Charité University Medicine Berlin Germany University of Bern Bern Switzerland Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Softsensor.ai BridgewaterNJ United States PRR.ai TX United States Department of R&d Center Arontier Co. Ltd Seoul Korea Republic of Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Cnrs Ifce Inrae Université de Tours Prc Nouzilly3780 France Institut de Génétique et de Biologie Moléculaire et Cellulaire Illkirch France Centre National de la Recherche Scientifique UMR7104 Illkirch France Institut National de la Santé et de la Recherche Médicale Inserm U1258 Illkirch France Université de Strasbourg Strasbourg France Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany School of Software Engineering South China University of Technology Guangzhou China Department of Pathology University Hospitals Coventry Warwickshire Nhs Trust Coventry United Kingdom Division of Biomedical Sciences Warwick Medical School University of Warwick Coventry United Kingdom

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery. © 2023, CC BY-NC-SA.

关键词： Grading

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NIMG-29. ASSOCIATION OF PARTIAL T2-FLAIR MISMATCH SIGN AND ISOCITRATE DEHYDROGENASE MUTATION IN WHO GRADE 4 GLIOMA/GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM

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Neuro-Oncology 2022年第SUPPLEMENT_7期24卷 vii168–vii169页

作者： Lee, Matthew Sako, Chiharu Patel, Sohil Mohan, Suyash Balana, Carmen Barnholtz-Sloan, Jill Sloan, Andrew Badve, Chaitra Poisson, Laila Griffith, Brent Booth, Thomas Palmer, Joshua Chakravarti, Arnab Bakas, Spyridon Nasrallah, MacLean Choi, Yoon Seong Dicker, Adam Flanders, Adam Shi, Wenyin Mahajan, Abhishek Colen, Rivka Marcus, Daniel Orringer, Daniel Davatzikos, Christos Jain, Rajan Department of Radiology NYU Grossman School of Medicine New York NY USA Center for Biomedical Image Computing and Analytics and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia USA Department of Radiology University of Virginia School of Medicine Charlottesville USA Center for Biomedical Image Computing and Analytics Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Medical Oncology Department Catalan Institute of Oncology Barcelona Spain Center for Biomedical Informatics and Information Technology and Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Pathology and Department of Neurosurgery Case Western Reserve University and University Hospitals Cleveland Medical Center Seidman Cancer Center and Case Comprehensive Cancer Center Cleveland USA Department of Radiology Case Western Reserve University and University Hospitals Cleveland Medical Center Cleveland USA Department of Public Health Sciences Center for Bioinformatics Henry Ford Health System Detroit MI USA Department of Radiology Henry Ford Health System Detroit MI USA School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom The Department of Radiation Oncology The James Cancer Hospital Ohio State University Wexner Medical Center Columbus OH USA Department of Radiation Oncology Ohio State University Wexner Medical Center Columbus OH USA Center for Biomedical Image Computing and Analytics Department of Radiology and Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Yonsei University College of Medicine Seoul Republic of Korea Department of Radiation Oncology Sidney Kimmel Cancer Center Thomas Jefferson

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