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Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization

Qaiser Mahmood, Artur Chodorowski, Andrew Mehnert, Johanna Gellermann, Mikael Persson
2014 Journal of digital imaging  
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

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
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

L.P. Clarke, R.P. Velthuizen, M.A. Camacho, J.J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher, M.L. Silbiger
1995 Magnetic Resonance Imaging  
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

S. Y. Matthew Goh, Andrei Irimia, Carinna M. Torgerson, John D. Van Horn
2014 Frontiers in Neuroinformatics  
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

Hala Shaari, Jasmin Kevrić, Samed Jukić, Larisa Bešić, Dejan Jokić, Nuredin Ahmed, Vladimir Rajs
2021 Brain Sciences  
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

Ryad Zemouri, Noureddine Zerhouni, Daniel Racoceanu
2019 Applied Sciences  
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  
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

Dror Haor, Roman Joffe, Reuven Shavit, Ziv Peremen, Yaki Stern, Amir B. Geva
2018 Brain Topography  
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

Simon Van Eyndhoven, Borbála Hunyadi, Patrick Dupont, Wim Van Paesschen, Sabine Van Huffel
2019 Frontiers in Neurology  
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

Ayman El-Baz
2018 Frontiers in Bioscience  
(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

Q. Mahmood, A. Chodorowski, M. Persson
2015 IRBM  
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  
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]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
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

Aklima Akter Lima, M. Firoz Mridha, Sujoy Chandra Das, Muhammad Mohsin Kabir, Md. Rashedul Islam, Yutaka Watanobe
2022 Biology  
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

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
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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