The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Su Zhang - One of the best experts on this subject based on the ideXlab platform.
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automatic segmentation and volumetric quantification of white matter hyperintensities on fluid attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:Introduction This study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.
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Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:IntroductionThis study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.MethodsTwo EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method.ResultsThe Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset.ConclusionThe proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.
Rui Wang - One of the best experts on this subject based on the ideXlab platform.
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automatic segmentation and volumetric quantification of white matter hyperintensities on fluid attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:Introduction This study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.
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Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:IntroductionThis study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.MethodsTwo EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method.ResultsThe Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset.ConclusionThe proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.
Yuemin Zhu - One of the best experts on this subject based on the ideXlab platform.
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automatic segmentation and volumetric quantification of white matter hyperintensities on fluid attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:Introduction This study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.
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Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:IntroductionThis study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.MethodsTwo EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method.ResultsThe Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset.ConclusionThe proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.
Xiaoer Wei - One of the best experts on this subject based on the ideXlab platform.
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automatic segmentation and volumetric quantification of white matter hyperintensities on fluid attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:Introduction This study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.
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Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:IntroductionThis study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.MethodsTwo EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method.ResultsThe Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset.ConclusionThe proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.
Jie Wang - One of the best experts on this subject based on the ideXlab platform.
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automatic segmentation and volumetric quantification of white matter hyperintensities on fluid attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:Introduction This study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.
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Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the Extreme Value Distribution
Neuroradiology, 2015Co-Authors: Rui Wang, Jie Wang, Xiaoer Wei, Yuemin Zhu, Su ZhangAbstract:IntroductionThis study aims to develop an automatic segmentation framework on the basis of Extreme Value Distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.MethodsTwo EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method.ResultsThe Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset.ConclusionThe proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.