The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Denis Kouamé - One of the best experts on this subject based on the ideXlab platform.
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a tensor factorization method for 3 d super resolution with application to dental ct
IEEE Transactions on Medical Imaging, 2019Co-Authors: Janka Hatvani, Adrian Basarab, Jean-yves Tourneret, Miklós Gyöngy, Denis KouaméAbstract:Available super-resolution techniques for 3-D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large database of known low-resolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time—2 min compared to 2 h for a dental volume of $282\times 266\times392$ voxels. Furthermore, it also offers slightly improved quantitative results (peak signal-to-noise ratio and Segmentation Quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.
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A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT
IEEE Transactions on Medical Imaging, 2019Co-Authors: Janka Hatvani, Adrian Basarab, Jean-yves Tourneret, Miklós Gyöngy, Denis KouaméAbstract:Available super-resolution techniques for 3-D images are either computationally inefficient priorknowledge-based iterative techniques or deep learning methods which require a large database of known lowresolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time-2 min compared to 2 h for a dental volume of 282 × 266 × 392 voxels. Furthermore, it also offers slightly improved quantitative results (peak signal-to-noise ratio and Segmentation Quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.
Janka Hatvani - One of the best experts on this subject based on the ideXlab platform.
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a tensor factorization method for 3 d super resolution with application to dental ct
IEEE Transactions on Medical Imaging, 2019Co-Authors: Janka Hatvani, Adrian Basarab, Jean-yves Tourneret, Miklós Gyöngy, Denis KouaméAbstract:Available super-resolution techniques for 3-D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large database of known low-resolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time—2 min compared to 2 h for a dental volume of $282\times 266\times392$ voxels. Furthermore, it also offers slightly improved quantitative results (peak signal-to-noise ratio and Segmentation Quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.
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A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT
IEEE Transactions on Medical Imaging, 2019Co-Authors: Janka Hatvani, Adrian Basarab, Jean-yves Tourneret, Miklós Gyöngy, Denis KouaméAbstract:Available super-resolution techniques for 3-D images are either computationally inefficient priorknowledge-based iterative techniques or deep learning methods which require a large database of known lowresolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time-2 min compared to 2 h for a dental volume of 282 × 266 × 392 voxels. Furthermore, it also offers slightly improved quantitative results (peak signal-to-noise ratio and Segmentation Quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.
Nay Aung - One of the best experts on this subject based on the ideXlab platform.
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automated Quality control in image Segmentation application to the uk biobank cardiovascular magnetic resonance imaging study
Journal of Cardiovascular Magnetic Resonance, 2019Co-Authors: Robert Robinson, Ozan Oktay, Wenjia Bai, Vanya V Valindria, Mihir M Sanghvi, Nay Aung, Bernhard Kainz, Hideaki SuzukiAbstract:The trend towards large-scale studies including population imaging poses new challenges in terms of Quality control (QC). This is a particular issue when automatic processing tools such as image Segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each Segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a Segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting Segmentation Quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed Segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. We report results used for predicting Segmentation Quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high Quality Segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual Segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted Quality metrics and manual visual QC scores. We show that Reverse classification accuracy has the potential for accurate and fully automatic Segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.
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automated Quality control in image Segmentation application to the uk biobank cardiac mr imaging study
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Robert Robinson, Ozan Oktay, Wenjia Bai, Vanya V Valindria, Mihir M Sanghvi, Nay Aung, Jose Miguel Paiva, Bernhard Kainz, Hideaki Suzuki, Filip ZemrakAbstract:Background: The trend towards large-scale studies including population imaging poses new challenges in terms of Quality control (QC). This is a particular issue when automatic processing tools, e.g. image Segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each Segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a Segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting Segmentation Quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed Segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting Segmentation Quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high Quality Segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual Segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted Quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic Segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.
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real time prediction of Segmentation Quality
Medical Image Computing and Computer-Assisted Intervention, 2018Co-Authors: Robert Robinson, Ozan Oktay, Wenjia Bai, Vanya V Valindria, Mihir M Sanghvi, Nay Aung, Jose Miguel Paiva, Filip ZemrakAbstract:Recent advances in deep learning based image Segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image Quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict Segmentation Quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis.
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real time prediction of Segmentation Quality
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Robert Robinson, Ozan Oktay, Wenjia Bai, Vanya V Valindria, Mihir M Sanghvi, Nay Aung, Jose Miguel Paiva, Filip ZemrakAbstract:Recent advances in deep learning based image Segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image Quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict Segmentation Quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated Quality control for cardiovascular MR Segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accuracy for separating low and high Quality Segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated Quality obtained via a reverse testing strategy. We report an MAE=0.14 and 91% binary classification accuracy for this case. Predictions are obtained in real-time which, when combined with real-time Segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results.
Fernando Pereira - One of the best experts on this subject based on the ideXlab platform.
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objective evaluation of video Segmentation Quality
IEEE Transactions on Image Processing, 2003Co-Authors: Paulo Lobato Correia, Fernando PereiraAbstract:Video Segmentation assumes a major role in the context of object-based coding and description applications. Evaluating the adequacy of a Segmentation result for a given application is a requisite both to allow the appropriate selection of Segmentation algorithms as well as to adjust their parameters for optimal performance. Subjective testing, the current practice for the evaluation of video Segmentation Quality, is an expensive and time-consuming process. Objective Segmentation Quality evaluation techniques can alternatively be used; however, it is recognized that, so far, much less research effort has been devoted to this subject than to the development of Segmentation algorithms. This paper discusses the problem of video Segmentation Quality evaluation, proposing evaluation methodologies and objective Segmentation Quality metrics for individual objects as well as for complete Segmentation partitions. Both standalone and relative evaluation metrics are developed to cover the cases for which a reference Segmentation is missing or available for comparison.
Miklós Gyöngy - One of the best experts on this subject based on the ideXlab platform.
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a tensor factorization method for 3 d super resolution with application to dental ct
IEEE Transactions on Medical Imaging, 2019Co-Authors: Janka Hatvani, Adrian Basarab, Jean-yves Tourneret, Miklós Gyöngy, Denis KouaméAbstract:Available super-resolution techniques for 3-D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large database of known low-resolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time—2 min compared to 2 h for a dental volume of $282\times 266\times392$ voxels. Furthermore, it also offers slightly improved quantitative results (peak signal-to-noise ratio and Segmentation Quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.
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A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT
IEEE Transactions on Medical Imaging, 2019Co-Authors: Janka Hatvani, Adrian Basarab, Jean-yves Tourneret, Miklós Gyöngy, Denis KouaméAbstract:Available super-resolution techniques for 3-D images are either computationally inefficient priorknowledge-based iterative techniques or deep learning methods which require a large database of known lowresolution and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this paper, this factorization framework is investigated for single image resolution enhancement with an offline estimate of the system point spread function. The technique is applied to 3-D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-of-the-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time-2 min compared to 2 h for a dental volume of 282 × 266 × 392 voxels. Furthermore, it also offers slightly improved quantitative results (peak signal-to-noise ratio and Segmentation Quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes in its parameters, proposing an ease of use.