Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable fo... more Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic...
2013 IEEE International Conference on Image Processing, 2013
ABSTRACT In this work, we propose a novel approach for improving center of gravity (COG) estimati... more ABSTRACT In this work, we propose a novel approach for improving center of gravity (COG) estimation of the brain in magnetic resonance (MR) images, that uses 3D Haar-like features. We hypothesize that better pose estimation will advance the posterior skull-stripping results of the popular Brain Extraction Tool (BET). The proposed methodology is quantitatively validated in 20 T1- and T2-weighted images of the brain. As compared to the native BET COG algorithm, our method produced COGs 87.3% closer to the expected coordinates for the T1-weighted dataset, and importantly this resulted in an average enhancement of 15.4% to the accuracy of skull-stripping masks. As far the authors know, we are first in analyzing the impact of COG estimation over skull-stripping of MR images.
This paper investigates a representation language with flexibility inspired by probabilistic logi... more This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian net-works. The goal is to handle propositional and first-order constructs together with precise, im-precise, indeterminate and qualitative ...
Abstract. Graphical models that represent uncertainty through sets of probability measures are of... more Abstract. Graphical models that represent uncertainty through sets of probability measures are often referred to as credal networks. Polynomial-time exact inference methods are available only for polytree-structured binary credal networks. In this work, we approximate potentially intractable inferences in multiconnected binary networks by tractable inferences in polytree-structures. We propose a novel set-based structural variational inference method the SV2U algorithm. The SV2U algorithm is the first method that produces approximate inferences in large binary credal networks with theoretical solid convergence analysis and offers a promising way to handle continuous variables in credal networks.
Abstract. In this work we show how to generate random Bayesian networks and how to test inference... more Abstract. In this work we show how to generate random Bayesian networks and how to test inference algorithms using these samples. First, we present a new method to generate random networks through Markov chains. We then use random networks to investigate the ...
Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable fo... more Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic...
2013 IEEE International Conference on Image Processing, 2013
ABSTRACT In this work, we propose a novel approach for improving center of gravity (COG) estimati... more ABSTRACT In this work, we propose a novel approach for improving center of gravity (COG) estimation of the brain in magnetic resonance (MR) images, that uses 3D Haar-like features. We hypothesize that better pose estimation will advance the posterior skull-stripping results of the popular Brain Extraction Tool (BET). The proposed methodology is quantitatively validated in 20 T1- and T2-weighted images of the brain. As compared to the native BET COG algorithm, our method produced COGs 87.3% closer to the expected coordinates for the T1-weighted dataset, and importantly this resulted in an average enhancement of 15.4% to the accuracy of skull-stripping masks. As far the authors know, we are first in analyzing the impact of COG estimation over skull-stripping of MR images.
This paper investigates a representation language with flexibility inspired by probabilistic logi... more This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian net-works. The goal is to handle propositional and first-order constructs together with precise, im-precise, indeterminate and qualitative ...
Abstract. Graphical models that represent uncertainty through sets of probability measures are of... more Abstract. Graphical models that represent uncertainty through sets of probability measures are often referred to as credal networks. Polynomial-time exact inference methods are available only for polytree-structured binary credal networks. In this work, we approximate potentially intractable inferences in multiconnected binary networks by tractable inferences in polytree-structures. We propose a novel set-based structural variational inference method the SV2U algorithm. The SV2U algorithm is the first method that produces approximate inferences in large binary credal networks with theoretical solid convergence analysis and offers a promising way to handle continuous variables in credal networks.
Abstract. In this work we show how to generate random Bayesian networks and how to test inference... more Abstract. In this work we show how to generate random Bayesian networks and how to test inference algorithms using these samples. First, we present a new method to generate random networks through Markov chains. We then use random networks to investigate the ...
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Papers by Shinsuke Ide