Multi-view longitudinal CNN for multiple sclerosis lesion segmentation
In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR in...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Birenbaum, Ariel [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation - Liu, Xiang ELSEVIER, 2015, the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:65 ; year:2017 ; pages:111-118 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.engappai.2017.06.006 |
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ELV040503100 |
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| 520 | |a In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. | ||
| 520 | |a In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. | ||
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10.1016/j.engappai.2017.06.006 doi GBVA2017015000005.pica (DE-627)ELV040503100 (ELSEVIER)S0952-1976(17)30125-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Birenbaum, Ariel verfasserin aut Multi-view longitudinal CNN for multiple sclerosis lesion segmentation 2017transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. Multiple Sclerosis Elsevier CNN Elsevier Longitudinal Elsevier Segmentation Elsevier Greenspan, Hayit oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:65 year:2017 pages:111-118 extent:8 https://doi.org/10.1016/j.engappai.2017.06.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 65 2017 111-118 8 045F 004 |
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10.1016/j.engappai.2017.06.006 doi GBVA2017015000005.pica (DE-627)ELV040503100 (ELSEVIER)S0952-1976(17)30125-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Birenbaum, Ariel verfasserin aut Multi-view longitudinal CNN for multiple sclerosis lesion segmentation 2017transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. Multiple Sclerosis Elsevier CNN Elsevier Longitudinal Elsevier Segmentation Elsevier Greenspan, Hayit oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:65 year:2017 pages:111-118 extent:8 https://doi.org/10.1016/j.engappai.2017.06.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 65 2017 111-118 8 045F 004 |
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10.1016/j.engappai.2017.06.006 doi GBVA2017015000005.pica (DE-627)ELV040503100 (ELSEVIER)S0952-1976(17)30125-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Birenbaum, Ariel verfasserin aut Multi-view longitudinal CNN for multiple sclerosis lesion segmentation 2017transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. Multiple Sclerosis Elsevier CNN Elsevier Longitudinal Elsevier Segmentation Elsevier Greenspan, Hayit oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:65 year:2017 pages:111-118 extent:8 https://doi.org/10.1016/j.engappai.2017.06.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 65 2017 111-118 8 045F 004 |
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10.1016/j.engappai.2017.06.006 doi GBVA2017015000005.pica (DE-627)ELV040503100 (ELSEVIER)S0952-1976(17)30125-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Birenbaum, Ariel verfasserin aut Multi-view longitudinal CNN for multiple sclerosis lesion segmentation 2017transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. Multiple Sclerosis Elsevier CNN Elsevier Longitudinal Elsevier Segmentation Elsevier Greenspan, Hayit oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:65 year:2017 pages:111-118 extent:8 https://doi.org/10.1016/j.engappai.2017.06.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 65 2017 111-118 8 045F 004 |
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10.1016/j.engappai.2017.06.006 doi GBVA2017015000005.pica (DE-627)ELV040503100 (ELSEVIER)S0952-1976(17)30125-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Birenbaum, Ariel verfasserin aut Multi-view longitudinal CNN for multiple sclerosis lesion segmentation 2017transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. Multiple Sclerosis Elsevier CNN Elsevier Longitudinal Elsevier Segmentation Elsevier Greenspan, Hayit oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:65 year:2017 pages:111-118 extent:8 https://doi.org/10.1016/j.engappai.2017.06.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 65 2017 111-118 8 045F 004 |
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Birenbaum, Ariel |
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Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
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multi-view longitudinal cnn for multiple sclerosis lesion segmentation |
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Multi-view longitudinal CNN for multiple sclerosis lesion segmentation |
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In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. |
| abstractGer |
In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. |
| abstract_unstemmed |
In this work, a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation is presented. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. In the proposed scheme, MR intensities and White Matter (WM) priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks (CNN) are utilized for false positive reduction and final segmentation result. The proposed network uses longitudinal data, a novel contribution in the domain of MS lesion analysis. The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. |
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Multi-view longitudinal CNN for multiple sclerosis lesion segmentation |
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https://doi.org/10.1016/j.engappai.2017.06.006 |
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