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Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data [article]

Mahsa Dadar, Simon Duchesne
2020 bioRxiv   pre-print
However, the reliability of the produced segmentations when using multi-center and multi-scanner data remains understudied.  ...  Conclusions: Our comparisons provide a benchmark on the reliability of the publicly used tissue classification techniques and the amount of variability that can be expected when using large multi-center  ...  multi-center and multi-scanner datasets.  ... 
doi:10.1101/2020.01.28.922971 fatcat:7ajoqn34anh4foefjs34g3yxsa

Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data

Mahsa Dadar, Simon Duchesne
2020 NeuroImage  
However, the reliability of the produced segmentations when using multi-center and multi-scanner data remains understudied.  ...  Our comparisons provide a benchmark on the reliability of the publicly used tissue classification techniques and the amount of variability that can be expected when using large multi-center datasets and  ...  Such assessments are particularly important for the field of neuroimaging, given that at present many researchers are transitioning to using large multi-center and multi-scanner databases in order to test  ... 
doi:10.1016/j.neuroimage.2020.116928 pmid:32413463 fatcat:i7rvnsffmzhcpg2mtn6hmhs2nu

Assessment of Reliability of Multi-site Neuroimaging Via Traveling Phantom Study [chapter]

Sylvain Gouttard, Martin Styner, Marcel Prastawa, Joseph Piven, Guido Gerig
2008 Lecture Notes in Computer Science  
This paper describes a framework for quantitative analysis of neuroimaging data of traveling human phantoms used for cross-site validation.  ...  Knowledge about such variability is crucial for study design and power analysis in new multi-site clinical studies.  ...  This research was supported in part by the National Institutes of Health under Grant RO1 HD055741 (Autism Center of Excellence, project IBIS), and in part by the National Alliance for Medical Image Computing  ... 
doi:10.1007/978-3-540-85990-1_32 fatcat:utrehgy7evhrxj3n5rbyj6fgde

Fully automated analysis using BRAINS: AutoWorkup

Ronald Pierson, Hans Johnson, Gregory Harris, Helen Keefe, Jane S. Paulsen, Nancy C. Andreasen, Vincent A. Magnotta
2011 NeuroImage  
In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences.  ...  Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.  ...  Douglas Langbehn for the statistical analysis of the PREDICT-HD multisite study data.  ... 
doi:10.1016/j.neuroimage.2010.06.047 pmid:20600977 pmcid:PMC3827877 fatcat:ujs2vimgofeu3kz3az65pczehi

BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier [article]

Mahsa Dadar, D. Louis Collins
2019 bioRxiv   pre-print
in large multi-center and multi-scanner databases.  ...  Conclusion: Our results show that BISON can provide accurate and robust segmentations in data from different age ranges and various scanner models, making it ideal for performing tissue classification  ...  This multi-center and multi-scanner training and validation ensure the generalizability of the results to data from different scanners and age ranges.  ... 
doi:10.1101/747998 fatcat:fpbgys6ulffazbnscnwjvwfpea

Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies [article]

Thomas Küstner, Tobias Hepp, Marc Fischer, Martin Schwartz, Andreas Fritsche, Hans-Ulrich Häring, Konstantin Nikolaou, Fabian Bamberg, Bin Yang, Fritz Schick, Sergios Gatidis, Jürgen Machann
2020 arXiv   pre-print
Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI.  ...  For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is  ...  Acknowledgements The work was supported in part by a grant (01GI0925) from the German Federal Ministry of Education  ... 
arXiv:2008.02251v1 fatcat:egrvlizw6fccdkvfgdaflubcju

A longitudinal human phantom reliability study of multi-center T1-weighted, DTI, and resting state fMRI data

Colin Hawco, Joseph D. Viviano, Sofia Chavez, Erin W. Dickie, Navona Calarco, Peter Kochunov, Miklos Argyelan, Jessica A. Turner, Anil K. Malhotra, Robert W. Buchanan, Aristotle N. Voineskos
2018 Psychiatry Research : Neuroimaging  
Multi-center MRI studies can enhance power, generalizability, and discovery for clinical neuroimaging research in brain disorders.  ...  With increasing interest in data-driven approaches in psychiatric and neurologic brain imaging studies, our findings provide a framework for multi-center analytic approaches aiming to identify subgroups  ...  The use of repeated multi-site scans can provide a more objective assessment for defining thresholds for rejecting data from a study.  ... 
doi:10.1016/j.pscychresns.2018.06.004 pmid:29945740 pmcid:PMC6482446 fatcat:zbfre24gfvclppt2t6ipwmyrwa

Preliminary analysis using multi-atlas labeling algorithms for tracing longitudinal change

Regina E. Y. Kim, Spencer Lourens, Jeffrey D. Long, Jane S. Paulsen, Hans J. Johnson
2015 Frontiers in Neuroscience  
In rare disease studies it is of primary importance to have a reliable tool that performs consistently for data from many different collection sites to increase study power.  ...  The present study examined the performance of multi-atlas labeling tools for subcortical identification using two types of in-vivo image database: Traveling Human Phantom (THP) and PREDICT-HD.  ...  (R01 EB000975), 3D Shape Analysis for Computational Anatomy (R01 EB008171), Neurobiological Predictors of HD (R01 NS040068), Cognitive and Functional Brain Changes in Preclinical HD (R01 NS054893), Algorithms  ... 
doi:10.3389/fnins.2015.00242 pmid:26236182 pmcid:PMC4500912 fatcat:nsvnyjve4nf6hfihfkvrp5wwoq

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker [article]

James H Cole, Rudra PK Poudel, Dimosthenis Tsagkrasoulis, Matthan WA Caan, Claire Steves, Tim D Spector, Giovanni Montana
2016 arXiv   pre-print
Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11).  ...  Brain-predicted age represents an accurate, highly reliable and genetically-valid phenotype, that has potential to be used as a biomarker of brain ageing.  ...  Reliability estimates varied for different combinations of input data and algorithm, however within-scanner test-retest reliability was high (ICC ≥ 0.90) for all analysis, even using raw data.  ... 
arXiv:1612.02572v1 fatcat:s7awsvx6cnb47ekhx5vsdqgu5u

A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate Cancer: High Grade vs. Low Grade

Jose M. Castillo T., Martijn P. A. Starmans, Muhammad Arif, Wiro J. Niessen, Stefan Klein, Chris H. Bangma, Ivo G. Schoots, Jifke F. Veenland
2021 Diagnostics  
The aim of this study is to evaluate the generalizability of radiomics models for prostate cancer classification and to compare the performance of these models to the performance of radiologists.  ...  For comparison with clinical practice, a multi-center classifier was tested and compared with the Prostate Imaging Reporting and Data System version 2 (PIRADS v2) scoring performed by two expert radiologists  ...  Conflicts of Interest: The authors declare no conflict of interest. Diagnostics 2021, 11, 369  ... 
doi:10.3390/diagnostics11020369 pmid:33671533 pmcid:PMC7926758 fatcat:rx74acdt2fgzhblpkwpvn7f55y

Learning brain MRI quality control: a multi-factorial generalization problem [article]

Ghiles Reguig, Marie Chupin, Hugo Dary, Eric Bardinet, Stéphane Lehéricy, Romain Valabregue
2022 arXiv   pre-print
We further analyzed the site-wise and study-wise predicted classification probability distributions of the models without preprocessing trained on ABIDE and CATI data.  ...  Our main results were that a model using features extracted from MRIQC without preprocessing yielded the best results when trained and evaluated on large multi-center datasets with a heterogeneous population  ...  studies to allow efficient and reliable meta-analyses.  ... 
arXiv:2205.15898v1 fatcat:ab4heuee5rg7xjekso4w3o7hjy

Automated Bayesian Segmentation of Microvascular White-Matter Lesions in the ACCORD-MIND Study

E Herskovits, R Bryan, F Yang
2008 Advances in Medical Sciences  
Conclusions: A Bayesian lesion-segmentation algorithm that collects multi-channel signal-intensity and spatial information from MR images of the brain shows potential for accurately segmenting brain lesions  ...  Conclusions: A Bayesian lesion-segmentation algorithm that collects multi-channel signal-intensity and spatial information from MR images of the brain shows potential for accurately segmenting brain lesions  ...  , and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Eye Institute, the National Institute on Aging, and the Centers for Disease Control and Prevention  ... 
doi:10.2478/v10039-008-0039-3 pmid:18842559 fatcat:qpcd5kxn5bfktnrwdcgxntee2m

Virtual Microscopy and Grid-Enabled Decision Support for Large-Scale Analysis of Imaged Pathology Specimens

Lin Yang, Wenjin Chen, P. Meer, G. Salaru, L.A. Goodell, V. Berstis, D.J. Foran
2009 IEEE Transactions on Information Technology in Biomedicine  
Using the proposed algorithm, a binary classification accuracy of 89% and the multi-class accuracy of 80% were achieved.  ...  As part of the Help Defeat Cancer (HDC) project, we have analyzed the data returned from WCG along with retrospective patient clinical profiles for a subset of 3744 breast tissue samples and the results  ...  The authors are also grateful to The Cancer Institute of New Jersey and the Hospital of the University of Pennsylvania for the specimens and support that they have provided for this research.  ... 
doi:10.1109/titb.2009.2020159 pmid:19369162 pmcid:PMC3683401 fatcat:tszzl345mnfe7f4goawqaruuqq

Guidelines for Developing Automated Quality Control Procedures for Brain Magnetic Resonance Images Acquired in Multi-Centre Clinical Trials [chapter]

Elias Gedamu
2011 Applications and Experiences of Quality Control  
of different MRI modalities, registration to brain atlases, brain tissue classification, segmentation of brain structures and types of pathology .  ...  For example, changes in echo times (TE) or repetition times (TR) can affect images contrast, and in turn, may modify the results of a tissue classification procedure.  ...  Guidelines for Developing Automated Quality Control Procedures for Brain Magnetic Resonance Images Acquired in Multi-Centre Clinical Trials, Applications and Experiences of Quality Control, Prof.  ... 
doi:10.5772/14515 fatcat:37d4nspayrc6jhu7mevonc6xam

Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning

Junghoon Lee, Aaron Carass, Amod Jog, Can Zhao, Jerry L. Prince, Elsa D. Angelini, Martin A. Styner
2017 Medical Imaging 2017: Image Processing  
Synthetic MR images are also computed from the registered atlas MRIs by using the same weights used for the CT synthesis; these are compared to the target patient MRIs allowing for the assessment of the  ...  The proposed approach was tested on brain cancer patient data, and showed a noticeable improvement for the tumor region.  ...  Currently, there are only a handful of MRI-based CT image synthesis methods, including tissue classification-based electron density assignment [5] , single or multi-atlas registration approaches [6]  ... 
doi:10.1117/12.2254571 pmid:29142336 pmcid:PMC5682626 dblp:conf/miip/LeeCJZP17 fatcat:lwuediiiurbwpji37feapjdaba
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