Automatic extraction of vertebral landmarks from ultrasound images: A pilot study

Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Brignol, A. [verfasserIn] Gueziri, H.E. [verfasserIn] Cheriet, F. [verfasserIn] Collins, D.L. [verfasserIn] Laporte, C. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Ultrasound imaging Spine Vertebra Landmarks Automatic detection

Übergeordnetes Werk:	Enthalten in: Computers in biology and medicine - Amsterdam [u.a.] : Elsevier Science, 1970, 122
Übergeordnetes Werk:	volume:122

DOI / URN:	10.1016/j.compbiomed.2020.103838

Katalog-ID:	ELV004348281

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520			\|a Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance.
650		4	\|a Ultrasound imaging
650		4	\|a Spine
650		4	\|a Vertebra
650		4	\|a Landmarks
650		4	\|a Automatic detection
700	1		\|a Gueziri, H.E. \|e verfasserin \|4 aut
700	1		\|a Cheriet, F. \|e verfasserin \|4 aut
700	1		\|a Collins, D.L. \|e verfasserin \|4 aut
700	1		\|a Laporte, C. \|e verfasserin \|4 aut
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allfields	10.1016/j.compbiomed.2020.103838 doi (DE-627)ELV004348281 (ELSEVIER)S0010-4825(20)30199-2 DE-627 ger DE-627 rda eng 610 570 DE-600 42.00 bkl 44.09 bkl Brignol, A. verfasserin aut Automatic extraction of vertebral landmarks from ultrasound images: A pilot study 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance. Ultrasound imaging Spine Vertebra Landmarks Automatic detection Gueziri, H.E. verfasserin aut Cheriet, F. verfasserin aut Collins, D.L. verfasserin aut Laporte, C. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 122 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2336 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_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.00 44.09 Medizintechnik AR 122
spelling	10.1016/j.compbiomed.2020.103838 doi (DE-627)ELV004348281 (ELSEVIER)S0010-4825(20)30199-2 DE-627 ger DE-627 rda eng 610 570 DE-600 42.00 bkl 44.09 bkl Brignol, A. verfasserin aut Automatic extraction of vertebral landmarks from ultrasound images: A pilot study 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance. Ultrasound imaging Spine Vertebra Landmarks Automatic detection Gueziri, H.E. verfasserin aut Cheriet, F. verfasserin aut Collins, D.L. verfasserin aut Laporte, C. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 122 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2336 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_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.00 44.09 Medizintechnik AR 122
allfields_unstemmed	10.1016/j.compbiomed.2020.103838 doi (DE-627)ELV004348281 (ELSEVIER)S0010-4825(20)30199-2 DE-627 ger DE-627 rda eng 610 570 DE-600 42.00 bkl 44.09 bkl Brignol, A. verfasserin aut Automatic extraction of vertebral landmarks from ultrasound images: A pilot study 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance. Ultrasound imaging Spine Vertebra Landmarks Automatic detection Gueziri, H.E. verfasserin aut Cheriet, F. verfasserin aut Collins, D.L. verfasserin aut Laporte, C. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 122 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2336 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_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.00 44.09 Medizintechnik AR 122
allfieldsGer	10.1016/j.compbiomed.2020.103838 doi (DE-627)ELV004348281 (ELSEVIER)S0010-4825(20)30199-2 DE-627 ger DE-627 rda eng 610 570 DE-600 42.00 bkl 44.09 bkl Brignol, A. verfasserin aut Automatic extraction of vertebral landmarks from ultrasound images: A pilot study 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance. Ultrasound imaging Spine Vertebra Landmarks Automatic detection Gueziri, H.E. verfasserin aut Cheriet, F. verfasserin aut Collins, D.L. verfasserin aut Laporte, C. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 122 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2336 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_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.00 44.09 Medizintechnik AR 122
allfieldsSound	10.1016/j.compbiomed.2020.103838 doi (DE-627)ELV004348281 (ELSEVIER)S0010-4825(20)30199-2 DE-627 ger DE-627 rda eng 610 570 DE-600 42.00 bkl 44.09 bkl Brignol, A. verfasserin aut Automatic extraction of vertebral landmarks from ultrasound images: A pilot study 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance. Ultrasound imaging Spine Vertebra Landmarks Automatic detection Gueziri, H.E. verfasserin aut Cheriet, F. verfasserin aut Collins, D.L. verfasserin aut Laporte, C. verfasserin aut Enthalten in Computers in biology and medicine Amsterdam [u.a.] : Elsevier Science, 1970 122 Online-Ressource (DE-627)306356783 (DE-600)1496984-1 (DE-576)081952988 1879-0534 nnns volume:122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2336 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_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.00 44.09 Medizintechnik AR 122
language	English
source	Enthalten in Computers in biology and medicine 122 volume:122
sourceStr	Enthalten in Computers in biology and medicine 122 volume:122
format_phy_str_mv	Article
bklname	Medizintechnik
institution	findex.gbv.de
topic_facet	Ultrasound imaging Spine Vertebra Landmarks Automatic detection
dewey-raw	610
isfreeaccess_bool	false
container_title	Computers in biology and medicine
authorswithroles_txt_mv	Brignol, A. @@aut@@ Gueziri, H.E. @@aut@@ Cheriet, F. @@aut@@ Collins, D.L. @@aut@@ Laporte, C. @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	306356783
dewey-sort	3610
id	ELV004348281
language_de	englisch
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author	Brignol, A.
spellingShingle	Brignol, A. ddc 610 bkl 42.00 bkl 44.09 misc Ultrasound imaging misc Spine misc Vertebra misc Landmarks misc Automatic detection Automatic extraction of vertebral landmarks from ultrasound images: A pilot study
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topic_title	610 570 DE-600 42.00 bkl 44.09 bkl Automatic extraction of vertebral landmarks from ultrasound images: A pilot study Ultrasound imaging Spine Vertebra Landmarks Automatic detection
topic	ddc 610 bkl 42.00 bkl 44.09 misc Ultrasound imaging misc Spine misc Vertebra misc Landmarks misc Automatic detection
topic_unstemmed	ddc 610 bkl 42.00 bkl 44.09 misc Ultrasound imaging misc Spine misc Vertebra misc Landmarks misc Automatic detection
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title	Automatic extraction of vertebral landmarks from ultrasound images: A pilot study
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title_full	Automatic extraction of vertebral landmarks from ultrasound images: A pilot study
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title_sort	automatic extraction of vertebral landmarks from ultrasound images: a pilot study
title_auth	Automatic extraction of vertebral landmarks from ultrasound images: A pilot study
abstract	Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance.
abstractGer	Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance.
abstract_unstemmed	Interpreting ultrasound (US) images of the spine is challenging due to the high variability of the contrast during freehand US acquisitions. In this paper, an automatic method to extract vertebral landmarks (spinous process and laminae) from US images acquired in the transverse plane is presented. Prior knowledge about the vertebral shape and the associated hyper-echoic property is incorporated using the horizontal and vertical projections of the image intensities. After detrending, the mean-value crossing of the projections is used to define the concept of mean boundary and locate landmarks without the need for thresholding or parameter adjustment. The method was evaluated using two datasets: a porcine cadaver dataset (PC) with CT data registered to the US data used as a gold standard, and a healthy human subjects dataset (HH) with a silver standard generated from manual landmarks located on the US data acquired with a curvilinear (6C2) and linear (14L5) probe. The mean sum of distances (MSD) of the landmark extraction to the gold and silver standards is respectively M S D = 0 . 90 ± 1 . 05 mm for PC, M S D = 1 . 14 ± 1 . 08 mm (6C2) and M S D = 3 . 54 ± 2 . 69 mm (14L5) for HH. Results are satisfying on PC and HH with 6C2. Variable contrast quality for 14L5 gives satisfying results for the spinous process but not for the laminae. The proposed approach has the potential to be used for different applications in the context of US spine imaging such as scoliosis follow-up and intra-operative surgical guidance.
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title_short	Automatic extraction of vertebral landmarks from ultrasound images: A pilot study
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up_date	2024-07-06T22:41:18.110Z
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Automatic extraction of vertebral landmarks from ultrasound images: A pilot study

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