The Experts below are selected from a list of 801 Experts worldwide ranked by ideXlab platform
Natalia Trayanova - One of the best experts on this subject based on the ideXlab platform.
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Estimation of ventricular fiber orientations in infarcted hearts for patient-specific simulations
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Farhad Pashakhanloo, Anthony Alers, Fady Dawoud, Karl H. Schuleri, Daniel Herzka, Elliot Mcveigh, Albert C. Lardo, Natalia TrayanovaAbstract:Patient-specific modeling of the heart is limited by lack of technology to acquire myocardial fiber orientations in the clinic. To overcome this limitation, we recently developed an image-based methodology to estimate the fiber orientations. In this study, we test the efficacy of that methodology in infarcted hearts. To this end, we implemented a processing pipeline to compare estimated fiber orientations of infarcted hearts with measured ones, and quantify the effect of the estimation error on outcomes of electrophysiological simulations. The pipeline was applied to images that we acquired from three infarcted canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of infarcted hearts that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
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ISBI - Estimation of ventricular fiber orientations in infarcted hearts for patient-specific simulations
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Farhad Pashakhanloo, Anthony Alers, Fady Dawoud, Karl H. Schuleri, Daniel Herzka, Elliot Mcveigh, Albert C. Lardo, Natalia TrayanovaAbstract:Patient-specific modeling of the heart is limited by lack of technology to acquire myocardial fiber orientations in the clinic. To overcome this limitation, we recently developed an image-based methodology to estimate the fiber orientations. In this study, we test the efficacy of that methodology in infarcted hearts. To this end, we implemented a processing pipeline to compare estimated fiber orientations of infarcted hearts with measured ones, and quantify the effect of the estimation error on outcomes of electrophysiological simulations. The pipeline was applied to images that we acquired from three infarcted canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of infarcted hearts that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
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EMBC - Image-based estimation of ventricular fiber orientations for patient-specific simulations
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Can Ceritoglu, Michael Miller, Natalia TrayanovaAbstract:Patient-specific simulation of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. In this research, we develop a methodology to predict ventricular fiber orientations of a patient heart, given the geometry of the heart and an atlas. We test the methodology by comparing the estimated fiber orientations with measured ones, and by quantifying the effect of the estimation error on outcomes of electrophysiological simulations, in normal and failing canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
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Image-based estimation of ventricular fiber orientations for patient-specific simulations
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Can Ceritoglu, Michael Miller, Natalia TrayanovaAbstract:Patient-specific simulation of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. In this research, we develop a methodology to predict ventricular fiber orientations of a patient heart, given the geometry of the heart and an atlas. We test the methodology by comparing the estimated fiber orientations with measured ones, and by quantifying the effect of the estimation error on outcomes of electrophysiological simulations, in normal and failing canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
Robert Keynton - One of the best experts on this subject based on the ideXlab platform.
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ICPR - Significant Region-Based Framework for Early Diagnosis of Alzheimer's Disease Using 11 C PiB-PET Scans
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Fatma El-zahraa A. El-gamal, Mohammed M. Elmogy, Ahmed Atwan, Mohammed Ghazal, Gregory N. Barnes, Hassan Hajjdiab, Robert KeyntonAbstract:Alzheimer's disease (AD) is a behavioral and cognitive neurodegenerative disorder whose sufferers exceed 5.5 million Americans. Among its stages, the early Diagnosis of AD is considered the main research issue due to many factors including the variable effects of the disease through its patients. This paper targets the Personalized Diagnosis of AD through presenting a local/regional analysis system that represents the degree of regional abnormalities using detailed parcellation of the brain. For more detailed results, the statistical analysis was applied for restricting the Diagnosis to the statistically determined significant brain regions. The system's evaluation shows promising results with an average accuracy, specificity, and sensitivity between the three tested groups of 98%, 99.09%, and 96.48%, respectively.
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Significant Region-Based Framework for Early Diagnosis of Alzheimer's Disease Using 11C PiB-PET Scans
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Fatma El-zahraa A. El-gamal, Mohammed M. Elmogy, Ahmed Atwan, Mohammed Ghazal, Gregory N. Barnes, Hassan Hajjdiab, Robert KeyntonAbstract:Alzheimer's disease (AD) is a behavioral and cognitive neurodegenerative disorder whose sufferers exceed 5.5 million Americans. Among its stages, the early Diagnosis of AD is considered the main research issue due to many factors including the variable effects of the disease through its patients. This paper targets the Personalized Diagnosis of AD through presenting a local/regional analysis system that represents the degree of regional abnormalities using detailed parcellation of the brain. For more detailed results, the statistical analysis was applied for restricting the Diagnosis to the statistically determined significant brain regions. The system's evaluation shows promising results with an average accuracy, specificity, and sensitivity between the three tested groups of 98%, 99.09%, and 96.48%, respectively.
Fijoy Vadakkumpadan - One of the best experts on this subject based on the ideXlab platform.
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Estimation of ventricular fiber orientations in infarcted hearts for patient-specific simulations
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Farhad Pashakhanloo, Anthony Alers, Fady Dawoud, Karl H. Schuleri, Daniel Herzka, Elliot Mcveigh, Albert C. Lardo, Natalia TrayanovaAbstract:Patient-specific modeling of the heart is limited by lack of technology to acquire myocardial fiber orientations in the clinic. To overcome this limitation, we recently developed an image-based methodology to estimate the fiber orientations. In this study, we test the efficacy of that methodology in infarcted hearts. To this end, we implemented a processing pipeline to compare estimated fiber orientations of infarcted hearts with measured ones, and quantify the effect of the estimation error on outcomes of electrophysiological simulations. The pipeline was applied to images that we acquired from three infarcted canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of infarcted hearts that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
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ISBI - Estimation of ventricular fiber orientations in infarcted hearts for patient-specific simulations
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Farhad Pashakhanloo, Anthony Alers, Fady Dawoud, Karl H. Schuleri, Daniel Herzka, Elliot Mcveigh, Albert C. Lardo, Natalia TrayanovaAbstract:Patient-specific modeling of the heart is limited by lack of technology to acquire myocardial fiber orientations in the clinic. To overcome this limitation, we recently developed an image-based methodology to estimate the fiber orientations. In this study, we test the efficacy of that methodology in infarcted hearts. To this end, we implemented a processing pipeline to compare estimated fiber orientations of infarcted hearts with measured ones, and quantify the effect of the estimation error on outcomes of electrophysiological simulations. The pipeline was applied to images that we acquired from three infarcted canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of infarcted hearts that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
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EMBC - Image-based estimation of ventricular fiber orientations for patient-specific simulations
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Can Ceritoglu, Michael Miller, Natalia TrayanovaAbstract:Patient-specific simulation of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. In this research, we develop a methodology to predict ventricular fiber orientations of a patient heart, given the geometry of the heart and an atlas. We test the methodology by comparing the estimated fiber orientations with measured ones, and by quantifying the effect of the estimation error on outcomes of electrophysiological simulations, in normal and failing canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
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Image-based estimation of ventricular fiber orientations for patient-specific simulations
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Fijoy Vadakkumpadan, Hermenegild Arevalo, Can Ceritoglu, Michael Miller, Natalia TrayanovaAbstract:Patient-specific simulation of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. In this research, we develop a methodology to predict ventricular fiber orientations of a patient heart, given the geometry of the heart and an atlas. We test the methodology by comparing the estimated fiber orientations with measured ones, and by quantifying the effect of the estimation error on outcomes of electrophysiological simulations, in normal and failing canine hearts. The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in Personalized Diagnosis and decisions regarding electrophysiological interventions.
Benjamin Haibekains - One of the best experts on this subject based on the ideXlab platform.
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mm2s Personalized Diagnosis of medulloblastoma patients and model systems
Source Code for Biology and Medicine, 2016Co-Authors: Deena M.a. Gendoo, Benjamin HaibekainsAbstract:Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for Personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes ( MM2S ) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers. The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes. Our MM2S package can be used to generate predictions without having to rely on an external web server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/web/packages/MM2S/ , as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab.
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Personalized Diagnosis of medulloblastoma subtypes across patients and model systems
Genomics, 2015Co-Authors: Deena M.a. Gendoo, Benjamin Haibekains, Petr Smirnov, Mathieu LupienAbstract:Abstract Molecular subtyping is instrumental towards selection of model systems for fundamental research in tumor pathogenesis, and clinical patient assessment. Medulloblastoma (MB) is a highly heterogeneous, malignant brain tumor that is the most common cause of cancer-related deaths in children. Current MB classification schemes require large sample sizes, and standard reference samples, for subtype predictions. Such approaches are impractical in clinical settings with limited tumor biopsies, and unsuitable for model system predictions where standard reference samples are unavailable. Our developed Medullo-Model To Subtype (MM2S) classifier stratifies single MB gene expression profiles without reference samples or replicates. Our pathway-centric approach facilitates subtype predictions of patient samples, and model systems including cell lines and mouse models. MM2S demonstrates >96% accuracy for patients of well-characterized normal cerebellum, WNT, or SHH subtypes, and the less-characterized Group 4 (86%) and Group 3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.
Fatma El-zahraa A. El-gamal - One of the best experts on this subject based on the ideXlab platform.
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ICPR - Significant Region-Based Framework for Early Diagnosis of Alzheimer's Disease Using 11 C PiB-PET Scans
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Fatma El-zahraa A. El-gamal, Mohammed M. Elmogy, Ahmed Atwan, Mohammed Ghazal, Gregory N. Barnes, Hassan Hajjdiab, Robert KeyntonAbstract:Alzheimer's disease (AD) is a behavioral and cognitive neurodegenerative disorder whose sufferers exceed 5.5 million Americans. Among its stages, the early Diagnosis of AD is considered the main research issue due to many factors including the variable effects of the disease through its patients. This paper targets the Personalized Diagnosis of AD through presenting a local/regional analysis system that represents the degree of regional abnormalities using detailed parcellation of the brain. For more detailed results, the statistical analysis was applied for restricting the Diagnosis to the statistically determined significant brain regions. The system's evaluation shows promising results with an average accuracy, specificity, and sensitivity between the three tested groups of 98%, 99.09%, and 96.48%, respectively.
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Significant Region-Based Framework for Early Diagnosis of Alzheimer's Disease Using 11C PiB-PET Scans
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Fatma El-zahraa A. El-gamal, Mohammed M. Elmogy, Ahmed Atwan, Mohammed Ghazal, Gregory N. Barnes, Hassan Hajjdiab, Robert KeyntonAbstract:Alzheimer's disease (AD) is a behavioral and cognitive neurodegenerative disorder whose sufferers exceed 5.5 million Americans. Among its stages, the early Diagnosis of AD is considered the main research issue due to many factors including the variable effects of the disease through its patients. This paper targets the Personalized Diagnosis of AD through presenting a local/regional analysis system that represents the degree of regional abnormalities using detailed parcellation of the brain. For more detailed results, the statistical analysis was applied for restricting the Diagnosis to the statistically determined significant brain regions. The system's evaluation shows promising results with an average accuracy, specificity, and sensitivity between the three tested groups of 98%, 99.09%, and 96.48%, respectively.
