Magnetic resonance imaging standardization for accurate grading of cerebral gliomas
Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Consi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Guohua [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2021), 29 vom: 11. Jan., Seite 41477-41496 |
|---|---|
| Übergeordnetes Werk: |
volume:81 ; year:2021 ; number:29 ; day:11 ; month:01 ; pages:41477-41496 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-020-10487-3 |
|---|
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OLC2080057642 |
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| 520 | |a Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. | ||
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10.1007/s11042-020-10487-3 doi (DE-627)OLC2080057642 (DE-He213)s11042-020-10487-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhao, Guohua verfasserin (orcid)0000-0002-8813-8527 aut Magnetic resonance imaging standardization for accurate grading of cerebral gliomas 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. Data standardization Histogram specification Grid search MRI Multicenter Bai, Jie (orcid)0000-0002-7744-1117 aut Yang, Guan (orcid)0000-0002-1659-2819 aut Shi, Lei (orcid)0000-0002-0655-3971 aut Tao, Yongcai (orcid)0000-0003-3098-3960 aut Cheng, Jingliang (orcid)0000-0001-9944-8419 aut Lin, Yusong (orcid)0000-0002-2284-5763 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2021), 29 vom: 11. Jan., Seite 41477-41496 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2021 number:29 day:11 month:01 pages:41477-41496 https://doi.org/10.1007/s11042-020-10487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2021 29 11 01 41477-41496 |
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10.1007/s11042-020-10487-3 doi (DE-627)OLC2080057642 (DE-He213)s11042-020-10487-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhao, Guohua verfasserin (orcid)0000-0002-8813-8527 aut Magnetic resonance imaging standardization for accurate grading of cerebral gliomas 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. Data standardization Histogram specification Grid search MRI Multicenter Bai, Jie (orcid)0000-0002-7744-1117 aut Yang, Guan (orcid)0000-0002-1659-2819 aut Shi, Lei (orcid)0000-0002-0655-3971 aut Tao, Yongcai (orcid)0000-0003-3098-3960 aut Cheng, Jingliang (orcid)0000-0001-9944-8419 aut Lin, Yusong (orcid)0000-0002-2284-5763 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2021), 29 vom: 11. Jan., Seite 41477-41496 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2021 number:29 day:11 month:01 pages:41477-41496 https://doi.org/10.1007/s11042-020-10487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2021 29 11 01 41477-41496 |
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10.1007/s11042-020-10487-3 doi (DE-627)OLC2080057642 (DE-He213)s11042-020-10487-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhao, Guohua verfasserin (orcid)0000-0002-8813-8527 aut Magnetic resonance imaging standardization for accurate grading of cerebral gliomas 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. Data standardization Histogram specification Grid search MRI Multicenter Bai, Jie (orcid)0000-0002-7744-1117 aut Yang, Guan (orcid)0000-0002-1659-2819 aut Shi, Lei (orcid)0000-0002-0655-3971 aut Tao, Yongcai (orcid)0000-0003-3098-3960 aut Cheng, Jingliang (orcid)0000-0001-9944-8419 aut Lin, Yusong (orcid)0000-0002-2284-5763 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2021), 29 vom: 11. Jan., Seite 41477-41496 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2021 number:29 day:11 month:01 pages:41477-41496 https://doi.org/10.1007/s11042-020-10487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2021 29 11 01 41477-41496 |
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10.1007/s11042-020-10487-3 doi (DE-627)OLC2080057642 (DE-He213)s11042-020-10487-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhao, Guohua verfasserin (orcid)0000-0002-8813-8527 aut Magnetic resonance imaging standardization for accurate grading of cerebral gliomas 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. Data standardization Histogram specification Grid search MRI Multicenter Bai, Jie (orcid)0000-0002-7744-1117 aut Yang, Guan (orcid)0000-0002-1659-2819 aut Shi, Lei (orcid)0000-0002-0655-3971 aut Tao, Yongcai (orcid)0000-0003-3098-3960 aut Cheng, Jingliang (orcid)0000-0001-9944-8419 aut Lin, Yusong (orcid)0000-0002-2284-5763 aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2021), 29 vom: 11. Jan., Seite 41477-41496 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2021 number:29 day:11 month:01 pages:41477-41496 https://doi.org/10.1007/s11042-020-10487-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2021 29 11 01 41477-41496 |
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Zhao, Guohua Bai, Jie Yang, Guan Shi, Lei Tao, Yongcai Cheng, Jingliang Lin, Yusong |
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Zhao, Guohua |
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10.1007/s11042-020-10487-3 |
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magnetic resonance imaging standardization for accurate grading of cerebral gliomas |
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Magnetic resonance imaging standardization for accurate grading of cerebral gliomas |
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Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Abstract Computer-aided diagnosis has attracted attention for the accurate grading of cerebral glioma. Most algorithms are only effective in relatively large datasets. Although multicenter data sharing is expanding, the results of cerebral glioma grading are not promising for multicenter data. Considering that multicenter images differ in contrast, we propose an effective image standardization method to reduce the disparity in image contrast of different datasets. The method is adopted in multiple sets of comparative experiments on a public dataset (BraTS2017) and a local dataset. The classification accuracy of experimental data relative to that of multicenter data without image normalization is improved by approximately 25% on average. Results demonstrate that the proposed approach is effective in solving the image contrast disparity of multicenter data. It also addresses the challenge of limited effective sample size in accurate cerebral glioma grading. The novel image standardization technology proposed in this work is a promising solution that can be integrated into expert systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Magnetic resonance imaging standardization for accurate grading of cerebral gliomas |
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Bai, Jie Yang, Guan Shi, Lei Tao, Yongcai Cheng, Jingliang Lin, Yusong |
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