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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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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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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece 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 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 Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial 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 Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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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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Discovering Alzheimer’s disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Zhijian Yang Junhao Wen Ahmed Abdulkadir Yuhan Cui Guray Erus Elizabeth Mamourian Randa Melhem Dhivya Srinivasan Sindhuja Tirumalai Govindarajan Jiong Chen Mohamad Habes Colin L Masters Paul Maruff Jurgen Fripp Luigi Ferrucci Marilyn S. Albert Sterling C Johnson John C Morris Pamela LaMontagne Daniel S. Marcus Tammie L.S. Benzinger David A. Wolk Li Shen Jingxuan Bao Susan M. Resnick Duygu Tosun Haochang Shou Ilya M. Nasrallah Christos Davatzikos the Alzheimer’s Disease Neuroimaging Initiative, the Preclinical AD Consortium, the Baltimore Longitudinal Study of Aging, and the iSTAGING study 1Artificial 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 Correspondece 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 University of Bern Bern Switzerland 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 Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of Texas Health San Antonio San Antonio TX USA Florey Institute of Neuroscience and Mental Health Parkville VIC Australia The Florey Institute of Neuroscience and Mental Health University of Melbourne Parkville VIC Australia CSIRO Health and Biosecurity Australian E-Health Research Centre Brisbane QLD Australia National Institute on Aging NIH Baltimore MD USA Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA Wisconsin Alzheimer’s Institute University of Wisconsin-Madison School of Medicine and Public Health Madison WI USA Knight Alzheimer Disease Research Center St. Louis MO USA Washington University School of Medicine Saint Louis MO USA Washington University School of Medicine St. Louis MO USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Institute for Biomedical Informatics University of Pennsylvania ATHENS PA USA University of Pennsylvania Philadelphia PA USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Department of Radiology and Biomedical Imaging University of Cal

Background The heterogeneity of neurodegenerative diseases, including Alzheimer’s disease (AD), has hampered precision diagnosis and treatment. Machine learning methods enable the identification of genetically-explained disease subtypes with distinct brain phenotypes. Method Gene-SGAN is a novel, multi-view, weakly-supervised deep clustering method that jointly considers phenotypic and genetic data, thereby deriving subtypes of disease-related brain changes with genetic associations. We apply the method to derive subtypes related to AD. Analyses used structural MRIs and whole-genome sequencing data from the Alzheimer’s Disease Neuroimaging Initiative (1,533 participants, including controls and patients with Mild Cognitive Impairment (MCI) and AD). Result Gene-SGAN identified four distinct subtypes within MCI/AD: A1, normal anatomy; A2, focal medial temporal lobe (MTL) atrophy; A3, severe atrophy in neocortex and MTL; A4, severe cortical but relatively modest MTL atrophy (Figure 1). The four subtypes reveal significant differences in seven known AD-related genetic variants (Figure 2). The identified subtype-associated SNPs not only resemble previous findings, but also suggest genetic protective effects contributing to the observed resilience (A1), potential inflammatory mechanisms underlying subtypes (A2&A3), and the effect of SNPs on atypical atrophy patterns of AD (A4). Clinically, A1 and A3 participants showed the best and the worst cognitive performances (Figure 3). A3 participants had the most abnormal CSF Aβ (p<0.001 vs. A1&A4 and p = 0.039 vs. A2), and exhibited the highest white matter hyperintensity (WMH) volumes (p<0.001 vs. A1&A4 and p = 0.017 vs. A2). However, A2 participants showed significantly higher CSF phospho-tau than A3 participants (p = 0.019). A4 participants were the youngest group (p<0.001 vs. all other groups), suggesting the inclusion of early onset AD participants, who have a higher prevalence of hippocampal-sparing variants such as posterio

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NIMG-33. PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: UPDATES FROM THE RESPOND CONSORTIUM

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Neuro-Oncology 2022年第SUPPLEMENT_7期24卷 vii170–vii170页

作者： Akbari, Hamed Bakas, Spyridon Sako, Chiharu Kazerooni, Anahita Fathi Villanueva-Meyer, Javier Garcia, Jose Bagley, Stephen Baid, Ujjwal Bilello, Michel Brem, Steven Lustig, Robert Mohan, Suyash Nasrallah, MacLean O'Rourke, Donald Calabrese, Evan Rudie, Jeffrey LaMontagne, Pamela Marcus, Daniel Balana, Carmen Capellades, Jaume Puig, Josep Barnholtz-Sloan, Jill Badve, Chaitra Sloan, Andrew Ak, Murat Colen, Rivka Ahn, Sung Soo Chang, Jong Hee Choi, Yoon Seong Lee, Seung-Koo Dicker, Adam Flanders, Adam Shi, Wenyin Shukla, Gaurav Griffith, Brent Poisson, Laila Rogers, Lisa Booth, Thomas Jain, Rajan Lee, Matthew Mahajan, Abhishek Chakravarti, Arnab Palmer, Joshua Taylor, William Cepeda, Santiago Wiestler, Benedikt Davatzikos, Christos University of Pennsylvania Philadelphia PA 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 Center for Biomedical Image Computing and Analytics and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia USA University of Pennsylvania Philadelphia USA University of California San Francisco San Francisco CA USA Hospital of the University of Pennsylvania Philadelphia PA USA Hospital of the University of Pennsylvania Philadelphia USA Center for Biomedical Image Computing and Analytics Department of Radiology 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 University of California San Francisco San Francisco USA Washington University School of Medicine St. Louis USA Department of Radiology Washington University School of Medicine St. Louis MO USA Medical Oncology Department Catalan Institute of Oncology Barcelona Spain Department of Medical Imaging Consorci MAR Parc de Salut Barcelona Spain Department of Radiology (IDI) and Girona Biomedical Research Institute (IDIBGI) Hospital Universitari de Girona Dr Josep Trueta Girona Spain Center for Biomedical Informatics and Information Technology and Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Radiology Case Western Reserve University and University Hospitals Cleveland Medical Center Cleveland 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 University of Pittsburgh Pittsburgh USA Department of Radiology University of Pittsburgh Pittsburgh PA USA Yons

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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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NIMG-67. MULTI-PARAMETRIC MRI-BASED MACHINE LEARNING ANALYSIS FOR PREDICTION OF NEOPLASTIC INFILTRATION AND RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: UPDATES FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

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Neuro-Oncology 2022年第SUPPLEMENT_7期24卷 vii179–vii180页

作者： Akbari, Hamed Mohan, Suyash Garcia, Jose Kazerooni, Anahita Fathi Sako, Chiharu Bakas, Spyridon Bilello, Michel Bagley, Stephen Baid, Ujjwal Brem, Steven Lustig, Robert Nasrallah, MacLean O'Rourke, Donald Barnholtz-Sloan, Jill Badve, Chaitra Sloan, Andrew Jain, Rajan Lee, Matthew Chakravarti, Arnab Palmer, Joshua Taylor, William Cepeda, Santiago Dicker, Adam Flanders, Adam Shi, Wenyin Shukla, Gaurav Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier LaMontagne, Pamela Marcus, Daniel Balana, Carmen Capellades, Jaume Puig, Josep Ak, Murat Colen, Rivka Ahn, Sung Soo Chang, Jong Hee Choi, Yoon Seong Lee, Seung-Koo Griffith, Brent Poisson, Laila Rogers, Lisa Booth, Thomas Mahajan, Abhishek Wiestler, Benedikt Davatzikos, Christos University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia USA Center for Biomedical Image Computing and Analytics and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia 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 Hospital of the University of Pennsylvania Philadelphia PA USA Hospital of the University of Pennsylvania Philadelphia USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Biomedical Informatics and Information Technology and Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Radiology Case Western Reserve University and University Hospitals Cleveland Medical Center Cleveland 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 NYU Grossman School of Medicine New York NY USA Department of Radiation Oncology Ohio State University Wexner Medical Center Columbus OH USA The Department of Radiation Oncology The James Cancer Hospital Ohio State University Wexner Medical Center Columbus OH USA The Ohio State University Columbus USA University Hospital Río Hortega Valladolid Spain Department of Radiation Oncology Sidney Kimmel Cancer Center Thomas Jefferson University Philadelphia PA USA Department of Radiology Sidney Kimmel Cancer Center Thomas Jefferson University Philadelphia PA USA Department of Radiation Oncology Thomas Jefferson University Hospita

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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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Biomedical image analysis competitions: The state of current participation practice

arXiv

引用

arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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