Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm
Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gupta, Abhishek [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© CARS 2015 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer assisted radiology and surgery - Berlin : Springer, 2006, 11(2015), 7 vom: 24. Dez., Seite 1297-1309 |
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| Übergeordnetes Werk: |
volume:11 ; year:2015 ; number:7 ; day:24 ; month:12 ; pages:1297-1309 |
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| DOI / URN: |
10.1007/s11548-015-1334-7 |
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| Katalog-ID: |
SPR020708920 |
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| 100 | 1 | |a Gupta, Abhishek |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm |
| 264 | 1 | |c 2015 | |
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| 520 | |a Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. | ||
| 650 | 4 | |a 3D cephalometry |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Automatic landmarking |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Knowledge-based detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CBCT |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cephalometric analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kharbanda, Om Prakash |4 aut | |
| 700 | 1 | |a Sardana, Viren |4 aut | |
| 700 | 1 | |a Balachandran, Rajiv |4 aut | |
| 700 | 1 | |a Sardana, Harish Kumar |4 aut | |
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10.1007/s11548-015-1334-7 doi (DE-627)SPR020708920 (SPR)s11548-015-1334-7-e DE-627 ger DE-627 rakwb eng Gupta, Abhishek verfasserin aut Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2015 Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. 3D cephalometry (dpeaa)DE-He213 Automatic landmarking (dpeaa)DE-He213 Knowledge-based detection (dpeaa)DE-He213 CBCT (dpeaa)DE-He213 Cephalometric analysis (dpeaa)DE-He213 Kharbanda, Om Prakash aut Sardana, Viren aut Balachandran, Rajiv aut Sardana, Harish Kumar aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 11(2015), 7 vom: 24. Dez., Seite 1297-1309 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:11 year:2015 number:7 day:24 month:12 pages:1297-1309 https://dx.doi.org/10.1007/s11548-015-1334-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2015 7 24 12 1297-1309 |
| spelling |
10.1007/s11548-015-1334-7 doi (DE-627)SPR020708920 (SPR)s11548-015-1334-7-e DE-627 ger DE-627 rakwb eng Gupta, Abhishek verfasserin aut Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2015 Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. 3D cephalometry (dpeaa)DE-He213 Automatic landmarking (dpeaa)DE-He213 Knowledge-based detection (dpeaa)DE-He213 CBCT (dpeaa)DE-He213 Cephalometric analysis (dpeaa)DE-He213 Kharbanda, Om Prakash aut Sardana, Viren aut Balachandran, Rajiv aut Sardana, Harish Kumar aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 11(2015), 7 vom: 24. Dez., Seite 1297-1309 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:11 year:2015 number:7 day:24 month:12 pages:1297-1309 https://dx.doi.org/10.1007/s11548-015-1334-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2015 7 24 12 1297-1309 |
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10.1007/s11548-015-1334-7 doi (DE-627)SPR020708920 (SPR)s11548-015-1334-7-e DE-627 ger DE-627 rakwb eng Gupta, Abhishek verfasserin aut Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2015 Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. 3D cephalometry (dpeaa)DE-He213 Automatic landmarking (dpeaa)DE-He213 Knowledge-based detection (dpeaa)DE-He213 CBCT (dpeaa)DE-He213 Cephalometric analysis (dpeaa)DE-He213 Kharbanda, Om Prakash aut Sardana, Viren aut Balachandran, Rajiv aut Sardana, Harish Kumar aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 11(2015), 7 vom: 24. Dez., Seite 1297-1309 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:11 year:2015 number:7 day:24 month:12 pages:1297-1309 https://dx.doi.org/10.1007/s11548-015-1334-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2015 7 24 12 1297-1309 |
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10.1007/s11548-015-1334-7 doi (DE-627)SPR020708920 (SPR)s11548-015-1334-7-e DE-627 ger DE-627 rakwb eng Gupta, Abhishek verfasserin aut Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2015 Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. 3D cephalometry (dpeaa)DE-He213 Automatic landmarking (dpeaa)DE-He213 Knowledge-based detection (dpeaa)DE-He213 CBCT (dpeaa)DE-He213 Cephalometric analysis (dpeaa)DE-He213 Kharbanda, Om Prakash aut Sardana, Viren aut Balachandran, Rajiv aut Sardana, Harish Kumar aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 11(2015), 7 vom: 24. Dez., Seite 1297-1309 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:11 year:2015 number:7 day:24 month:12 pages:1297-1309 https://dx.doi.org/10.1007/s11548-015-1334-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2015 7 24 12 1297-1309 |
| allfieldsSound |
10.1007/s11548-015-1334-7 doi (DE-627)SPR020708920 (SPR)s11548-015-1334-7-e DE-627 ger DE-627 rakwb eng Gupta, Abhishek verfasserin aut Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2015 Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. 3D cephalometry (dpeaa)DE-He213 Automatic landmarking (dpeaa)DE-He213 Knowledge-based detection (dpeaa)DE-He213 CBCT (dpeaa)DE-He213 Cephalometric analysis (dpeaa)DE-He213 Kharbanda, Om Prakash aut Sardana, Viren aut Balachandran, Rajiv aut Sardana, Harish Kumar aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 11(2015), 7 vom: 24. Dez., Seite 1297-1309 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:11 year:2015 number:7 day:24 month:12 pages:1297-1309 https://dx.doi.org/10.1007/s11548-015-1334-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_602 GBV_ILN_636 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_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_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_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2015 7 24 12 1297-1309 |
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English |
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Enthalten in International journal of computer assisted radiology and surgery 11(2015), 7 vom: 24. Dez., Seite 1297-1309 volume:11 year:2015 number:7 day:24 month:12 pages:1297-1309 |
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3D cephalometry Automatic landmarking Knowledge-based detection CBCT Cephalometric analysis |
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International journal of computer assisted radiology and surgery |
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Gupta, Abhishek @@aut@@ Kharbanda, Om Prakash @@aut@@ Sardana, Viren @@aut@@ Balachandran, Rajiv @@aut@@ Sardana, Harish Kumar @@aut@@ |
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Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). 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Gupta, Abhishek |
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Gupta, Abhishek misc 3D cephalometry misc Automatic landmarking misc Knowledge-based detection misc CBCT misc Cephalometric analysis Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm |
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Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm 3D cephalometry (dpeaa)DE-He213 Automatic landmarking (dpeaa)DE-He213 Knowledge-based detection (dpeaa)DE-He213 CBCT (dpeaa)DE-He213 Cephalometric analysis (dpeaa)DE-He213 |
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misc 3D cephalometry misc Automatic landmarking misc Knowledge-based detection misc CBCT misc Cephalometric analysis |
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Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm |
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Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm |
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Gupta, Abhishek |
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International journal of computer assisted radiology and surgery |
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International journal of computer assisted radiology and surgery |
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Gupta, Abhishek |
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10.1007/s11548-015-1334-7 |
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accuracy of 3d cephalometric measurements based on an automatic knowledge-based landmark detection algorithm |
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Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm |
| abstract |
Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. © CARS 2015 |
| abstractGer |
Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. © CARS 2015 |
| abstract_unstemmed |
Purpose To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and %$\hbox {Go}_{\mathrm{R}}\hbox {-Gn/S-N}%$). Conclusion Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification. © CARS 2015 |
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Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm |
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https://dx.doi.org/10.1007/s11548-015-1334-7 |
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Kharbanda, Om Prakash Sardana, Viren Balachandran, Rajiv Sardana, Harish Kumar |
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Kharbanda, Om Prakash Sardana, Viren Balachandran, Rajiv Sardana, Harish Kumar |
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| doi_str |
10.1007/s11548-015-1334-7 |
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2024-07-03T17:45:17.023Z |
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Methods The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. Results Inter-observer ICC for each landmark was found to be excellent (%${>}0.9%$) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm (%$\hbox {Or}_{\mathrm{L}}\hbox {-Or}_{\mathrm{R}}%$ distance) and %$2.12^{\circ }%$ (%$\hbox {Co}_{\mathrm{L}}\hbox {-Go}_{\mathrm{L}}%$-Me angle), respectively. 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|
| score |
7.40038 |