The Experts below are selected from a list of 324738 Experts worldwide ranked by ideXlab platform
Jianhua Yao - One of the best experts on this subject based on the ideXlab platform.
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tumor growth prediction with reaction diffusion and hyperelastic biomechanical model by Physiological Data fusion
Medical Image Analysis, 2015Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by Physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological Data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical Data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient Data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively.
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tumor growth prediction with hyperelastic biomechanical model Physiological Data fusion and nonlinear optimization
Medical Image Computing and Computer-Assisted Intervention, 2014Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:Tumor growth prediction is usually achieved by Physiological modeling and model personalization from clinical measurements. Although image-based frameworks have been proposed with promising results, different issues such as infinitesimal strain assumption, complicated optimization procedures, and lack of functional information, may limit the prediction performance. Therefore, we propose a framework which comprises a hyperelastic biomechanical model for better Physiological plausibility, gradient-free nonlinear optimization for more flexible choices of models and objective functions, and Physiological Data fusion of structural and functional images for better subject-specificity. Experiments were performed on synthetic and clinical Data to verify parameter estimation capability and prediction performance of the framework. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results on eight patient Data sets, the recall, precision, and relative volume difference (RVD) between predicted and measured tumor volumes are 84.85±6.15%, 87.08±7.83%, and 13.81±6.64% respectively.
Ken C L Wong - One of the best experts on this subject based on the ideXlab platform.
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tumor growth prediction with reaction diffusion and hyperelastic biomechanical model by Physiological Data fusion
Medical Image Analysis, 2015Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by Physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological Data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical Data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient Data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively.
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tumor growth prediction with hyperelastic biomechanical model Physiological Data fusion and nonlinear optimization
Medical Image Computing and Computer-Assisted Intervention, 2014Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:Tumor growth prediction is usually achieved by Physiological modeling and model personalization from clinical measurements. Although image-based frameworks have been proposed with promising results, different issues such as infinitesimal strain assumption, complicated optimization procedures, and lack of functional information, may limit the prediction performance. Therefore, we propose a framework which comprises a hyperelastic biomechanical model for better Physiological plausibility, gradient-free nonlinear optimization for more flexible choices of models and objective functions, and Physiological Data fusion of structural and functional images for better subject-specificity. Experiments were performed on synthetic and clinical Data to verify parameter estimation capability and prediction performance of the framework. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results on eight patient Data sets, the recall, precision, and relative volume difference (RVD) between predicted and measured tumor volumes are 84.85±6.15%, 87.08±7.83%, and 13.81±6.64% respectively.
Ronald M Summers - One of the best experts on this subject based on the ideXlab platform.
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tumor growth prediction with reaction diffusion and hyperelastic biomechanical model by Physiological Data fusion
Medical Image Analysis, 2015Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by Physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological Data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical Data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient Data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively.
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tumor growth prediction with hyperelastic biomechanical model Physiological Data fusion and nonlinear optimization
Medical Image Computing and Computer-Assisted Intervention, 2014Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:Tumor growth prediction is usually achieved by Physiological modeling and model personalization from clinical measurements. Although image-based frameworks have been proposed with promising results, different issues such as infinitesimal strain assumption, complicated optimization procedures, and lack of functional information, may limit the prediction performance. Therefore, we propose a framework which comprises a hyperelastic biomechanical model for better Physiological plausibility, gradient-free nonlinear optimization for more flexible choices of models and objective functions, and Physiological Data fusion of structural and functional images for better subject-specificity. Experiments were performed on synthetic and clinical Data to verify parameter estimation capability and prediction performance of the framework. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results on eight patient Data sets, the recall, precision, and relative volume difference (RVD) between predicted and measured tumor volumes are 84.85±6.15%, 87.08±7.83%, and 13.81±6.64% respectively.
Electron Kebebew - One of the best experts on this subject based on the ideXlab platform.
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tumor growth prediction with reaction diffusion and hyperelastic biomechanical model by Physiological Data fusion
Medical Image Analysis, 2015Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by Physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological Data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical Data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient Data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively.
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tumor growth prediction with hyperelastic biomechanical model Physiological Data fusion and nonlinear optimization
Medical Image Computing and Computer-Assisted Intervention, 2014Co-Authors: Ken C L Wong, Ronald M Summers, Electron Kebebew, Jianhua YaoAbstract:Tumor growth prediction is usually achieved by Physiological modeling and model personalization from clinical measurements. Although image-based frameworks have been proposed with promising results, different issues such as infinitesimal strain assumption, complicated optimization procedures, and lack of functional information, may limit the prediction performance. Therefore, we propose a framework which comprises a hyperelastic biomechanical model for better Physiological plausibility, gradient-free nonlinear optimization for more flexible choices of models and objective functions, and Physiological Data fusion of structural and functional images for better subject-specificity. Experiments were performed on synthetic and clinical Data to verify parameter estimation capability and prediction performance of the framework. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results on eight patient Data sets, the recall, precision, and relative volume difference (RVD) between predicted and measured tumor volumes are 84.85±6.15%, 87.08±7.83%, and 13.81±6.64% respectively.
James Brusey - One of the best experts on this subject based on the ideXlab platform.
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leveraging knowledge from Physiological Data on body heat stress risk prediction with sensor networks
IEEE Transactions on Biomedical Circuits and Systems, 2013Co-Authors: Elena Gaura, John Kemp, James BruseyAbstract:The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical Data and have accuracies of 92.1 ± 2.9% and 94.4 ± 2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.
