A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine
Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gao, Wei [verfasserIn] Schlüter, Steffen [verfasserIn] Blaser, Sebastian R. G. A. [verfasserIn] Shen, Jianbo [verfasserIn] Vetterlein, Doris [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
High-throughput root phenotyping |
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| Übergeordnetes Werk: |
Enthalten in: Plant and soil - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948, 441(2019), 1-2 vom: 18. Juni, Seite 643-655 |
|---|---|
| Übergeordnetes Werk: |
volume:441 ; year:2019 ; number:1-2 ; day:18 ; month:06 ; pages:643-655 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11104-019-04053-6 |
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| Katalog-ID: |
SPR016755200 |
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| 245 | 1 | 2 | |a A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine |
| 264 | 1 | |c 2019 | |
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| 520 | |a Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. | ||
| 650 | 4 | |a High-throughput root phenotyping |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Root segmentation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Root system architecture (RSA) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tubular root |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a X-ray computed tomography (CT) |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Schlüter, Steffen |e verfasserin |4 aut | |
| 700 | 1 | |a Blaser, Sebastian R. G. A. |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Jianbo |e verfasserin |4 aut | |
| 700 | 1 | |a Vetterlein, Doris |e verfasserin |4 aut | |
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10.1007/s11104-019-04053-6 doi (DE-627)SPR016755200 (SPR)s11104-019-04053-6-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Gao, Wei verfasserin aut A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. High-throughput root phenotyping (dpeaa)DE-He213 Image analysis (dpeaa)DE-He213 Root segmentation (dpeaa)DE-He213 Root system architecture (RSA) (dpeaa)DE-He213 Tubular root (dpeaa)DE-He213 X-ray computed tomography (CT) (dpeaa)DE-He213 Schlüter, Steffen verfasserin aut Blaser, Sebastian R. G. A. verfasserin aut Shen, Jianbo verfasserin aut Vetterlein, Doris verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 441(2019), 1-2 vom: 18. Juni, Seite 643-655 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:441 year:2019 number:1-2 day:18 month:06 pages:643-655 https://dx.doi.org/10.1007/s11104-019-04053-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 441 2019 1-2 18 06 643-655 |
| spelling |
10.1007/s11104-019-04053-6 doi (DE-627)SPR016755200 (SPR)s11104-019-04053-6-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Gao, Wei verfasserin aut A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. High-throughput root phenotyping (dpeaa)DE-He213 Image analysis (dpeaa)DE-He213 Root segmentation (dpeaa)DE-He213 Root system architecture (RSA) (dpeaa)DE-He213 Tubular root (dpeaa)DE-He213 X-ray computed tomography (CT) (dpeaa)DE-He213 Schlüter, Steffen verfasserin aut Blaser, Sebastian R. G. A. verfasserin aut Shen, Jianbo verfasserin aut Vetterlein, Doris verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 441(2019), 1-2 vom: 18. Juni, Seite 643-655 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:441 year:2019 number:1-2 day:18 month:06 pages:643-655 https://dx.doi.org/10.1007/s11104-019-04053-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 441 2019 1-2 18 06 643-655 |
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10.1007/s11104-019-04053-6 doi (DE-627)SPR016755200 (SPR)s11104-019-04053-6-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Gao, Wei verfasserin aut A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. High-throughput root phenotyping (dpeaa)DE-He213 Image analysis (dpeaa)DE-He213 Root segmentation (dpeaa)DE-He213 Root system architecture (RSA) (dpeaa)DE-He213 Tubular root (dpeaa)DE-He213 X-ray computed tomography (CT) (dpeaa)DE-He213 Schlüter, Steffen verfasserin aut Blaser, Sebastian R. G. A. verfasserin aut Shen, Jianbo verfasserin aut Vetterlein, Doris verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 441(2019), 1-2 vom: 18. Juni, Seite 643-655 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:441 year:2019 number:1-2 day:18 month:06 pages:643-655 https://dx.doi.org/10.1007/s11104-019-04053-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 441 2019 1-2 18 06 643-655 |
| allfieldsGer |
10.1007/s11104-019-04053-6 doi (DE-627)SPR016755200 (SPR)s11104-019-04053-6-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Gao, Wei verfasserin aut A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. High-throughput root phenotyping (dpeaa)DE-He213 Image analysis (dpeaa)DE-He213 Root segmentation (dpeaa)DE-He213 Root system architecture (RSA) (dpeaa)DE-He213 Tubular root (dpeaa)DE-He213 X-ray computed tomography (CT) (dpeaa)DE-He213 Schlüter, Steffen verfasserin aut Blaser, Sebastian R. G. A. verfasserin aut Shen, Jianbo verfasserin aut Vetterlein, Doris verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 441(2019), 1-2 vom: 18. Juni, Seite 643-655 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:441 year:2019 number:1-2 day:18 month:06 pages:643-655 https://dx.doi.org/10.1007/s11104-019-04053-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 441 2019 1-2 18 06 643-655 |
| allfieldsSound |
10.1007/s11104-019-04053-6 doi (DE-627)SPR016755200 (SPR)s11104-019-04053-6-e DE-627 ger DE-627 rakwb eng 570 580 ASE 48.32 bkl 48.52 bkl Gao, Wei verfasserin aut A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. High-throughput root phenotyping (dpeaa)DE-He213 Image analysis (dpeaa)DE-He213 Root segmentation (dpeaa)DE-He213 Root system architecture (RSA) (dpeaa)DE-He213 Tubular root (dpeaa)DE-He213 X-ray computed tomography (CT) (dpeaa)DE-He213 Schlüter, Steffen verfasserin aut Blaser, Sebastian R. G. A. verfasserin aut Shen, Jianbo verfasserin aut Vetterlein, Doris verfasserin aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 441(2019), 1-2 vom: 18. Juni, Seite 643-655 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:441 year:2019 number:1-2 day:18 month:06 pages:643-655 https://dx.doi.org/10.1007/s11104-019-04053-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 48.32 ASE 48.52 ASE AR 441 2019 1-2 18 06 643-655 |
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Enthalten in Plant and soil 441(2019), 1-2 vom: 18. Juni, Seite 643-655 volume:441 year:2019 number:1-2 day:18 month:06 pages:643-655 |
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High-throughput root phenotyping Image analysis Root segmentation Root system architecture (RSA) Tubular root X-ray computed tomography (CT) |
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Gao, Wei @@aut@@ Schlüter, Steffen @@aut@@ Blaser, Sebastian R. G. A. @@aut@@ Shen, Jianbo @@aut@@ Vetterlein, Doris @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR016755200</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519192841.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11104-019-04053-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR016755200</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11104-019-04053-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">580</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">48.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">48.52</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gao, Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. 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|
| author |
Gao, Wei |
| spellingShingle |
Gao, Wei ddc 570 bkl 48.32 bkl 48.52 misc High-throughput root phenotyping misc Image analysis misc Root segmentation misc Root system architecture (RSA) misc Tubular root misc X-ray computed tomography (CT) A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine |
| authorStr |
Gao, Wei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)270934979 |
| format |
electronic Article |
| dewey-ones |
570 - Life sciences; biology 580 - Plants (Botany) |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1573-5036 |
| topic_title |
570 580 ASE 48.32 bkl 48.52 bkl A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine High-throughput root phenotyping (dpeaa)DE-He213 Image analysis (dpeaa)DE-He213 Root segmentation (dpeaa)DE-He213 Root system architecture (RSA) (dpeaa)DE-He213 Tubular root (dpeaa)DE-He213 X-ray computed tomography (CT) (dpeaa)DE-He213 |
| topic |
ddc 570 bkl 48.32 bkl 48.52 misc High-throughput root phenotyping misc Image analysis misc Root segmentation misc Root system architecture (RSA) misc Tubular root misc X-ray computed tomography (CT) |
| topic_unstemmed |
ddc 570 bkl 48.32 bkl 48.52 misc High-throughput root phenotyping misc Image analysis misc Root segmentation misc Root system architecture (RSA) misc Tubular root misc X-ray computed tomography (CT) |
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A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine |
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A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine |
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Gao, Wei Schlüter, Steffen Blaser, Sebastian R. G. A. Shen, Jianbo Vetterlein, Doris |
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shape-based method for automatic and rapid segmentation of roots in soil from x-ray computed tomography images: rootine |
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A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine |
| abstract |
Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. |
| abstractGer |
Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. |
| abstract_unstemmed |
Aims X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify the spatio-temporal dynamics of RSA still remains a challenge due to non-automatic, time-consuming image processing protocols and their poor recovery of fine roots in soil. Methods Here we present a new protocol (Rootine) to segment roots rapidly and precisely down to fine roots with two voxels in diameter (90 μm in pots with 70 mm in diameter). This is facilitated by feature detection of the tubular shape of roots, an approach that was originally developed for detecting blood vessels in medical imaging. Results In comparison to established root segmentation methods, Rootine produced a more accurate root network, i.e. more roots and less over-segmentation. Root length quantified by X-ray CT showed high correlation with results by root washing combined with 2D light scanning ($ R^{2} $ = 0.92). Tests with different soil materials showed that the recovery of roots depends on signal-to-noise ratio but can be up to 99% for a favorable contrast between fine roots and background. Conclusions This new protocol provides great efficiency to study RSA in undisturbed soil. As it is fully automated it has the potential for high-throughput root phenotyping and related modelling. |
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| score |
7.4002457 |