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MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
2015
Computational Intelligence and Neuroscience
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We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain ...
Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. ...
of study participants and MRI acquisition at the UMC Utrecht ...
doi:10.1155/2015/813696
pmid:26759553
pmcid:PMC4680055
fatcat:uhsmxrfz65ahtc7fciqt6v3fem
Simulation and Validation in Brain Image Analysis
2016
Computational Intelligence and Neuroscience
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Acknowledgments We thank all the authors for submitting their papers to this special issue as well as the reviewers for providing their expertise and time to evaluate and improve the papers. ...
In "MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans" A. M. ...
Mendrik et al. describe an online platform to evaluate tissue segmentation in structural brain magnetic resonance imaging (MRI). ...
doi:10.1155/2016/1041058
pmid:27433159
pmcid:PMC4940532
fatcat:4pgunkb3ona2hiosa4uult46yi
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions
2017
Journal of digital imaging
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This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. ...
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. ...
MRBrainS The aim of the MRBrainS evaluation framework is to compare algorithms for segmentation of gray matter, white matter, and cerebrospinal fluid on multi-sequence (T1weighted, T1-weighted-inversion ...
doi:10.1007/s10278-017-9983-4
pmid:28577131
pmcid:PMC5537095
fatcat:lekbdtmkx5cchmuutntacymrzu
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
[article]
2020
arXiv
pre-print
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Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. ...
However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. ...
As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ...
arXiv:2009.04009v1
fatcat:42c67fdsgvgh5n77upgqxrqlhu
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
2020
Medical Image Analysis
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Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. ...
However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. ...
In addition, the information required for brain tissue or pathology segmentation may come from different scans, leading to hetero-modal (i.e. more than one set of input imaging sequences) datasets. ...
doi:10.1016/j.media.2020.101862
pmid:33129151
pmcid:PMC7116853
fatcat:ggpf32trafcy7kcglafkpt2q24
Cardiac MR: From Theory to Practice
2022
Frontiers in Cardiovascular Medicine
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Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. ...
Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. ...
Image Analysis CMR image segmentation and quantitative evaluation can be a challenging, time-consuming and operator intensive task. ...
doi:10.3389/fcvm.2022.826283
pmid:35310962
pmcid:PMC8927633
fatcat:j3vd446nxnajvgajkhazwbvgia
Automated brain lesion segmentation in magnetic resonance images
2019
unpublished
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With this thesis, we provide a strong set of tools for the fully automatic segmentation of lesions in brain magnetic resonance imaging (MRI). ...
Brain Lesion Segmentation in the Literature The body of research of general lesion segmentation in brain MRI is too large to be exhaustively covered in a thesis. Fortunately, Garcia et al. ...
All lesions were evaluated by the algorithm (including images acquired at 1.5 and 3T MRI field strength). There were no cases that could not be processed by the algorithm. ...
doi:10.5451/unibas-007117711
fatcat:jl52hjelwbaebepcevpbx6v2ea
Neuroimaging Biomarkers for Youth At-Risk for Serious Mental Illness
[article]
2020
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The ultimate aim of this dissertation is to identify neurobiological biomarkers for SMI that will have diagnostic or informative value in the classification of risk stages. ...
), I investigate specific structural and functional brain changes accompanying various stages risk and evolution of SMI. ...
Recent initiatives promoting the development of brain segmentation algorithms 302-305
have resulted in a surge of novel deep learning algorithms 290 (for example MRBrainS 302 , list
available online ...
doi:10.11575/prism/38095
fatcat:ju4274vw5befhhgwe7er3sfjky