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Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization
2014
Journal of digital imaging
Preserved Fulltext
It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. ...
a patient-specific head conductivity model for electroencephalography (EEG) source localization. ...
Acknowledgments The author Mr. Mahmood acknowledges scholarship funding from the Higher Education Commission of Pakistan (HEC) and Chalmers University of Technology in support of this work. ...
doi:10.1007/s10278-014-9752-6
pmid:25533494
pmcid:PMC4501958
fatcat:kxe3x3idijgxnghtkpw3t6kuzu
Front Matter: Volume 12032
2022
Medical Imaging 2022: Image Processing
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of SPIE at the time of publication. ...
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication. ...
estimation of pancreatic duct from abdominal CT imagesx Proc. of SPIE Vol. 12032 1203201-10 C-MADA:
unsupervised cross-modality adversarial domain adaptation framework for medical image segmentation ...
doi:10.1117/12.2638192
fatcat:ikfgnjefaba2tpiamxoftyi6sa
MRI segmentation: Methods and applications
1995
Magnetic Resonance Imaging
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Particular emphasis is placed on the relative merits of single image versus multlspectral segmentation, and supervised versus unsupervised segmentation methods. ...
The application of MRI segmentation for tumor volume measurements during the course of therapy is presented here as an example, illustrating problems associated with inter-and intra-observer variations ...
Acknowledgment-This research was supported by a grant from NC1 (grant # lROlCA59425-01). ...
doi:10.1016/0730-725x(94)00124-l
pmid:7791545
fatcat:3iq7mze52fby3nivbrir4fgqve
Neuroinformatics challenges to the structural, connectomic, functional and electrophysiological multimodal imaging of human traumatic brain injury
2014
Frontiers in Neuroinformatics
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Within each of these, the availability of a wide range of neuroimaging modalities can be leveraged to fully understand the heterogeneity of TBI pathology; consequently, large-scale computer hardware resources ...
As a result, the need for TBI neuroimaging analysis methods has increased in recent years due to the recognition that spatiotemporal computational analyses of TBI evolution are useful for capturing the ...
ACKNOWLEDGMENTS The authors wish to thank the faculty and staff of the Institute for Neuroimaging and Informatics at the University of Southern California. ...
doi:10.3389/fninf.2014.00019
pmid:24616696
pmcid:PMC3935464
fatcat:acoj4l4sbrecfnn4j3zg2eiyzy
Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges
2021
Brain Sciences
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Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included. ...
This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/brainsci11060716
pmid:34071202
fatcat:usmduuhzyzcsrh7lgto3ejzbfu
Deep Learning in the Biomedical Applications: Recent and Future Status
2019
Applied Sciences
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In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue ...
), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). ...
[315] aimed to solve the problem of learning to segment for lung cancer tumors from MR images through domain adaptation from CT to MRI, where there are a reasonable number of labeled data in the source ...
doi:10.3390/app9081526
fatcat:srjvngtufbhstfcvn4mvhmrdve
2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25
2021
IEEE journal of biomedical and health informatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
., +, JBHI Sept. 2021 3460-3472 MCDCD: Multi-Source Unsupervised Domain Adaptation for Abnormal D., +, JBHI June 2021 2260-2272 in Fundus Images. ...
doi:10.1109/jbhi.2022.3140980
fatcat:ufig7b54gfftnj3mocspoqbzq4
Improved Back-Projection Cortical Potential Imaging by Multi-resolution Optimization Technique
2018
Brain Topography
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Electroencephalogram (EEG) has evolved to be a well-established tool for imaging brain activity. This progress is mainly due to the development of high-resolution (HR) EEG methods. ...
We also validated these results with true EEG data. Analyzing these EEGs, we have demonstrated the MR-CPI competence to correctly localize cortical activations in a real environment. ...
Acknowledgements This work was supported (in part) by Grant from the MAGNET program of the Israeli OCS and by ElMindA Ltd. ...
doi:10.1007/s10548-018-0668-1
pmid:30076487
fatcat:hqrgskpxwjeppduad5k7fwoutu
Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone With EEG-Correlated fMRI
2019
Frontiers in Neurology
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Methods: Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity ...
Results: The novel predictor, derived from the filtered EEG signals, allowed the detection of the ictal onset zone in a larger percentage of epileptic patients (92% vs. at most 83% for the other predictors ...
ACKNOWLEDGMENTS We thank the reviewers for their valuable suggestions and remarks, which have helped to improve the manuscript. ...
doi:10.3389/fneur.2019.00805
pmid:31428036
pmcid:PMC6688528
fatcat:nlxtx4jftbgfrlfeifliakadku
Medical imaging diagnosis of early Alzheimer rsquo s disease
2018
Frontiers in Bioscience
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(102) proposed a multi-modal multi-task (M3T) learning scheme for the purpose of joint prediction of multiple variables from multi-modal data through multi-task feature selection (MTFS) and © 1996-2018 ...
In addition to the absence of various modalities, most databases offer MR imaging modality for scientific research. ...
For example, the movement of patients during the image acquisition process introduces a source of the ...
doi:10.2741/4612
pmid:28930568
fatcat:6f5gzcdyireuro3ylaswz2p734
Automated MRI brain tissue segmentation based on mean shift and fuzzy c -means using a priori tissue probability maps
2015
IRBM
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This paper presents a novel fully automated unsupervised framework for the brain tissue segmentation in magnetic resonance (MR) images. ...
T1-weighted MR images, obtained from the IBSR repository. ...
Acknowledgements This work has been supported in part by the Chalmers University of Technology, Sweden (Grant # S2412010) and the Higher Education Commission (HEC) of Pakistan (Grant # PD-2007-1). ...
doi:10.1016/j.irbm.2015.01.007
fatcat:74ur463yrjgwlnaynatotlffoq
2013 Index IEEE Transactions on Biomedical Engineering Vol. 60
2013
IEEE Transactions on Biomedical Engineering
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2013, and items from previous years that were commented upon or corrected in 2013. ...
., +, TBME Apr. 2013 1090-1099 Reference-Based Source Separation Method For Identification of Brain Regions Involved in a Reference State From Intracerebral EEG. ...
doi:10.1109/tbme.2013.2295159
fatcat:kjadiysea5h55hjr7i3o6vstlm
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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We also outline the limitations of existing techniques and discuss potential directions for future research. ...
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. ...
GCNs can also propagate and exchange local information across the whole image to learn the semantic relationships between objects. 4) Multi-modal medical data analysis: Multi-modal neuroimage analysis ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders
2022
Biology
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A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. ...
In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron ...
Acknowledgments: We would like to thank Bangladesh University of Business & Technology (BUBT), University of Asia Pacific (UAP), and University of Aizu (UoA) for supporting this research. ...
doi:10.3390/biology11030469
pmid:35336842
pmcid:PMC8945195
fatcat:kjsv63tnhzbslfa6p4agcmarlq
An overview of deep learning in medical imaging focusing on MRI
2018
Zeitschrift für Medizinische Physik
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to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical ...
imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. ...
Acknowledgements We thank Renate Grüner for useful discussions. The anonymous reviewers gave us excellent constructive feedback that led to several improvements throughout the article. ...
doi:10.1016/j.zemedi.2018.11.002
fatcat:kkimovnwcrhmth7mg6h6cpomjm
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