An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the bra...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ebner, Michael [verfasserIn] Wang, Guotai [verfasserIn] Li, Wenqi [verfasserIn] Aertsen, Michael [verfasserIn] Patel, Premal A. [verfasserIn] Aughwane, Rosalind [verfasserIn] Melbourne, Andrew [verfasserIn] Doel, Tom [verfasserIn] Dymarkowski, Steven [verfasserIn] De Coppi, Paolo [verfasserIn] David, Anna L. [verfasserIn] Deprest, Jan [verfasserIn] Ourselin, Sébastien [verfasserIn] Vercauteren, Tom [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: NeuroImage - Orlando, Fla. : Academic Press, 1992, 206 |
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| Übergeordnetes Werk: |
volume:206 |
| DOI / URN: |
10.1016/j.neuroimage.2019.116324 |
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| Katalog-ID: |
ELV003494756 |
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| 100 | 1 | |a Ebner, Michael |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI |
| 264 | 1 | |c 2019 | |
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| 520 | |a High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. | ||
| 650 | 4 | |a Fetal MRI | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Super resolution | |
| 650 | 4 | |a Convolutional neural network | |
| 650 | 4 | |a Brain localization | |
| 650 | 4 | |a Segmentation | |
| 700 | 1 | |a Wang, Guotai |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Wenqi |e verfasserin |4 aut | |
| 700 | 1 | |a Aertsen, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Patel, Premal A. |e verfasserin |4 aut | |
| 700 | 1 | |a Aughwane, Rosalind |e verfasserin |4 aut | |
| 700 | 1 | |a Melbourne, Andrew |e verfasserin |4 aut | |
| 700 | 1 | |a Doel, Tom |e verfasserin |4 aut | |
| 700 | 1 | |a Dymarkowski, Steven |e verfasserin |4 aut | |
| 700 | 1 | |a De Coppi, Paolo |e verfasserin |4 aut | |
| 700 | 1 | |a David, Anna L. |e verfasserin |4 aut | |
| 700 | 1 | |a Deprest, Jan |e verfasserin |4 aut | |
| 700 | 1 | |a Ourselin, Sébastien |e verfasserin |4 aut | |
| 700 | 1 | |a Vercauteren, Tom |e verfasserin |4 aut | |
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10.1016/j.neuroimage.2019.116324 doi (DE-627)ELV003494756 (ELSEVIER)S1053-8119(19)30915-2 DE-627 ger DE-627 rda eng 610 DE-600 LING DE-30 fid 44.64 bkl 44.90 bkl Ebner, Michael verfasserin aut An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. Fetal MRI Deep learning Super resolution Convolutional neural network Brain localization Segmentation Wang, Guotai verfasserin aut Li, Wenqi verfasserin aut Aertsen, Michael verfasserin aut Patel, Premal A. verfasserin aut Aughwane, Rosalind verfasserin aut Melbourne, Andrew verfasserin aut Doel, Tom verfasserin aut Dymarkowski, Steven verfasserin aut De Coppi, Paolo verfasserin aut David, Anna L. verfasserin aut Deprest, Jan verfasserin aut Ourselin, Sébastien verfasserin aut Vercauteren, Tom verfasserin aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 206 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:206 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 Radiologie 44.90 Neurologie AR 206 |
| spelling |
10.1016/j.neuroimage.2019.116324 doi (DE-627)ELV003494756 (ELSEVIER)S1053-8119(19)30915-2 DE-627 ger DE-627 rda eng 610 DE-600 LING DE-30 fid 44.64 bkl 44.90 bkl Ebner, Michael verfasserin aut An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. Fetal MRI Deep learning Super resolution Convolutional neural network Brain localization Segmentation Wang, Guotai verfasserin aut Li, Wenqi verfasserin aut Aertsen, Michael verfasserin aut Patel, Premal A. verfasserin aut Aughwane, Rosalind verfasserin aut Melbourne, Andrew verfasserin aut Doel, Tom verfasserin aut Dymarkowski, Steven verfasserin aut De Coppi, Paolo verfasserin aut David, Anna L. verfasserin aut Deprest, Jan verfasserin aut Ourselin, Sébastien verfasserin aut Vercauteren, Tom verfasserin aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 206 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:206 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 Radiologie 44.90 Neurologie AR 206 |
| allfields_unstemmed |
10.1016/j.neuroimage.2019.116324 doi (DE-627)ELV003494756 (ELSEVIER)S1053-8119(19)30915-2 DE-627 ger DE-627 rda eng 610 DE-600 LING DE-30 fid 44.64 bkl 44.90 bkl Ebner, Michael verfasserin aut An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. Fetal MRI Deep learning Super resolution Convolutional neural network Brain localization Segmentation Wang, Guotai verfasserin aut Li, Wenqi verfasserin aut Aertsen, Michael verfasserin aut Patel, Premal A. verfasserin aut Aughwane, Rosalind verfasserin aut Melbourne, Andrew verfasserin aut Doel, Tom verfasserin aut Dymarkowski, Steven verfasserin aut De Coppi, Paolo verfasserin aut David, Anna L. verfasserin aut Deprest, Jan verfasserin aut Ourselin, Sébastien verfasserin aut Vercauteren, Tom verfasserin aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 206 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:206 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 Radiologie 44.90 Neurologie AR 206 |
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10.1016/j.neuroimage.2019.116324 doi (DE-627)ELV003494756 (ELSEVIER)S1053-8119(19)30915-2 DE-627 ger DE-627 rda eng 610 DE-600 LING DE-30 fid 44.64 bkl 44.90 bkl Ebner, Michael verfasserin aut An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. Fetal MRI Deep learning Super resolution Convolutional neural network Brain localization Segmentation Wang, Guotai verfasserin aut Li, Wenqi verfasserin aut Aertsen, Michael verfasserin aut Patel, Premal A. verfasserin aut Aughwane, Rosalind verfasserin aut Melbourne, Andrew verfasserin aut Doel, Tom verfasserin aut Dymarkowski, Steven verfasserin aut De Coppi, Paolo verfasserin aut David, Anna L. verfasserin aut Deprest, Jan verfasserin aut Ourselin, Sébastien verfasserin aut Vercauteren, Tom verfasserin aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 206 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:206 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 Radiologie 44.90 Neurologie AR 206 |
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10.1016/j.neuroimage.2019.116324 doi (DE-627)ELV003494756 (ELSEVIER)S1053-8119(19)30915-2 DE-627 ger DE-627 rda eng 610 DE-600 LING DE-30 fid 44.64 bkl 44.90 bkl Ebner, Michael verfasserin aut An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. Fetal MRI Deep learning Super resolution Convolutional neural network Brain localization Segmentation Wang, Guotai verfasserin aut Li, Wenqi verfasserin aut Aertsen, Michael verfasserin aut Patel, Premal A. verfasserin aut Aughwane, Rosalind verfasserin aut Melbourne, Andrew verfasserin aut Doel, Tom verfasserin aut Dymarkowski, Steven verfasserin aut De Coppi, Paolo verfasserin aut David, Anna L. verfasserin aut Deprest, Jan verfasserin aut Ourselin, Sébastien verfasserin aut Vercauteren, Tom verfasserin aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 206 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:206 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.64 Radiologie 44.90 Neurologie AR 206 |
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Radiologie Neurologie |
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Fetal MRI Deep learning Super resolution Convolutional neural network Brain localization Segmentation |
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Ebner, Michael @@aut@@ Wang, Guotai @@aut@@ Li, Wenqi @@aut@@ Aertsen, Michael @@aut@@ Patel, Premal A. @@aut@@ Aughwane, Rosalind @@aut@@ Melbourne, Andrew @@aut@@ Doel, Tom @@aut@@ Dymarkowski, Steven @@aut@@ De Coppi, Paolo @@aut@@ David, Anna L. @@aut@@ Deprest, Jan @@aut@@ Ourselin, Sébastien @@aut@@ Vercauteren, Tom @@aut@@ |
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2019-01-01T00:00:00Z |
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Ebner, Michael ddc 610 fid LING bkl 44.64 bkl 44.90 misc Fetal MRI misc Deep learning misc Super resolution misc Convolutional neural network misc Brain localization misc Segmentation An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI |
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610 DE-600 LING DE-30 fid 44.64 bkl 44.90 bkl An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI Fetal MRI Deep learning Super resolution Convolutional neural network Brain localization Segmentation |
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An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI |
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Ebner, Michael Wang, Guotai Li, Wenqi Aertsen, Michael Patel, Premal A. Aughwane, Rosalind Melbourne, Andrew Doel, Tom Dymarkowski, Steven De Coppi, Paolo David, Anna L. Deprest, Jan Ourselin, Sébastien Vercauteren, Tom |
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an automated framework for localization, segmentation and super-resolution reconstruction of fetal brain mri |
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An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI |
| abstract |
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. |
| abstractGer |
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. |
| abstract_unstemmed |
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice. |
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An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI |
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Wang, Guotai Li, Wenqi Aertsen, Michael Patel, Premal A. Aughwane, Rosalind Melbourne, Andrew Doel, Tom Dymarkowski, Steven De Coppi, Paolo David, Anna L. Deprest, Jan Ourselin, Sébastien Vercauteren, Tom |
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| score |
7.4002256 |