Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming

Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary...
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Autor*in:	Zahnd, Guillaume [verfasserIn] Karanasos, Antonios van Soest, Gijs Regar, Evelyn Niessen, Wiro Gijsen, Frank van Walsum, Theo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Coronary artery Optical coherence tomography Interventional imaging Thin-cap fibroatheroma Contour segmentation Dynamic programming Preoperative planning

Anmerkung:	© The Author(s) 2015

Übergeordnetes Werk:	Enthalten in: International journal of computer assisted radiology and surgery - Berlin : Springer, 2006, 10(2015), 9 vom: 05. März, Seite 1383-1394
Übergeordnetes Werk:	volume:10 ; year:2015 ; number:9 ; day:05 ; month:03 ; pages:1383-1394

Links:	Volltext

DOI / URN:	10.1007/s11548-015-1164-7

Katalog-ID:	SPR020706898

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100	1		\|a Zahnd, Guillaume \|e verfasserin \|4 aut
245	1	0	\|a Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
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520			\|a Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.
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650		4	\|a Optical coherence tomography \|7 (dpeaa)DE-He213
650		4	\|a Interventional imaging \|7 (dpeaa)DE-He213
650		4	\|a Thin-cap fibroatheroma \|7 (dpeaa)DE-He213
650		4	\|a Contour segmentation \|7 (dpeaa)DE-He213
650		4	\|a Dynamic programming \|7 (dpeaa)DE-He213
650		4	\|a Preoperative planning \|7 (dpeaa)DE-He213
700	1		\|a Karanasos, Antonios \|4 aut
700	1		\|a van Soest, Gijs \|4 aut
700	1		\|a Regar, Evelyn \|4 aut
700	1		\|a Niessen, Wiro \|4 aut
700	1		\|a Gijsen, Frank \|4 aut
700	1		\|a van Walsum, Theo \|4 aut
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allfields	10.1007/s11548-015-1164-7 doi (DE-627)SPR020706898 (SPR)s11548-015-1164-7-e DE-627 ger DE-627 rakwb eng Zahnd, Guillaume verfasserin aut Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2015 Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. Coronary artery (dpeaa)DE-He213 Optical coherence tomography (dpeaa)DE-He213 Interventional imaging (dpeaa)DE-He213 Thin-cap fibroatheroma (dpeaa)DE-He213 Contour segmentation (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Preoperative planning (dpeaa)DE-He213 Karanasos, Antonios aut van Soest, Gijs aut Regar, Evelyn aut Niessen, Wiro aut Gijsen, Frank aut van Walsum, Theo aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 10(2015), 9 vom: 05. März, Seite 1383-1394 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:10 year:2015 number:9 day:05 month:03 pages:1383-1394 https://dx.doi.org/10.1007/s11548-015-1164-7 kostenfrei 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 10 2015 9 05 03 1383-1394
spelling	10.1007/s11548-015-1164-7 doi (DE-627)SPR020706898 (SPR)s11548-015-1164-7-e DE-627 ger DE-627 rakwb eng Zahnd, Guillaume verfasserin aut Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2015 Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. Coronary artery (dpeaa)DE-He213 Optical coherence tomography (dpeaa)DE-He213 Interventional imaging (dpeaa)DE-He213 Thin-cap fibroatheroma (dpeaa)DE-He213 Contour segmentation (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Preoperative planning (dpeaa)DE-He213 Karanasos, Antonios aut van Soest, Gijs aut Regar, Evelyn aut Niessen, Wiro aut Gijsen, Frank aut van Walsum, Theo aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 10(2015), 9 vom: 05. März, Seite 1383-1394 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:10 year:2015 number:9 day:05 month:03 pages:1383-1394 https://dx.doi.org/10.1007/s11548-015-1164-7 kostenfrei 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 10 2015 9 05 03 1383-1394
allfields_unstemmed	10.1007/s11548-015-1164-7 doi (DE-627)SPR020706898 (SPR)s11548-015-1164-7-e DE-627 ger DE-627 rakwb eng Zahnd, Guillaume verfasserin aut Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2015 Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. Coronary artery (dpeaa)DE-He213 Optical coherence tomography (dpeaa)DE-He213 Interventional imaging (dpeaa)DE-He213 Thin-cap fibroatheroma (dpeaa)DE-He213 Contour segmentation (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Preoperative planning (dpeaa)DE-He213 Karanasos, Antonios aut van Soest, Gijs aut Regar, Evelyn aut Niessen, Wiro aut Gijsen, Frank aut van Walsum, Theo aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 10(2015), 9 vom: 05. März, Seite 1383-1394 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:10 year:2015 number:9 day:05 month:03 pages:1383-1394 https://dx.doi.org/10.1007/s11548-015-1164-7 kostenfrei 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 10 2015 9 05 03 1383-1394
allfieldsGer	10.1007/s11548-015-1164-7 doi (DE-627)SPR020706898 (SPR)s11548-015-1164-7-e DE-627 ger DE-627 rakwb eng Zahnd, Guillaume verfasserin aut Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2015 Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. Coronary artery (dpeaa)DE-He213 Optical coherence tomography (dpeaa)DE-He213 Interventional imaging (dpeaa)DE-He213 Thin-cap fibroatheroma (dpeaa)DE-He213 Contour segmentation (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Preoperative planning (dpeaa)DE-He213 Karanasos, Antonios aut van Soest, Gijs aut Regar, Evelyn aut Niessen, Wiro aut Gijsen, Frank aut van Walsum, Theo aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 10(2015), 9 vom: 05. März, Seite 1383-1394 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:10 year:2015 number:9 day:05 month:03 pages:1383-1394 https://dx.doi.org/10.1007/s11548-015-1164-7 kostenfrei 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 10 2015 9 05 03 1383-1394
allfieldsSound	10.1007/s11548-015-1164-7 doi (DE-627)SPR020706898 (SPR)s11548-015-1164-7-e DE-627 ger DE-627 rakwb eng Zahnd, Guillaume verfasserin aut Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2015 Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. Coronary artery (dpeaa)DE-He213 Optical coherence tomography (dpeaa)DE-He213 Interventional imaging (dpeaa)DE-He213 Thin-cap fibroatheroma (dpeaa)DE-He213 Contour segmentation (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Preoperative planning (dpeaa)DE-He213 Karanasos, Antonios aut van Soest, Gijs aut Regar, Evelyn aut Niessen, Wiro aut Gijsen, Frank aut van Walsum, Theo aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 10(2015), 9 vom: 05. März, Seite 1383-1394 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:10 year:2015 number:9 day:05 month:03 pages:1383-1394 https://dx.doi.org/10.1007/s11548-015-1164-7 kostenfrei 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 10 2015 9 05 03 1383-1394
language	English
source	Enthalten in International journal of computer assisted radiology and surgery 10(2015), 9 vom: 05. März, Seite 1383-1394 volume:10 year:2015 number:9 day:05 month:03 pages:1383-1394
sourceStr	Enthalten in International journal of computer assisted radiology and surgery 10(2015), 9 vom: 05. März, Seite 1383-1394 volume:10 year:2015 number:9 day:05 month:03 pages:1383-1394
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Coronary artery Optical coherence tomography Interventional imaging Thin-cap fibroatheroma Contour segmentation Dynamic programming Preoperative planning
isfreeaccess_bool	true
container_title	International journal of computer assisted radiology and surgery
authorswithroles_txt_mv	Zahnd, Guillaume @@aut@@ Karanasos, Antonios @@aut@@ van Soest, Gijs @@aut@@ Regar, Evelyn @@aut@@ Niessen, Wiro @@aut@@ Gijsen, Frank @@aut@@ van Walsum, Theo @@aut@@
publishDateDaySort_date	2015-03-05T00:00:00Z
hierarchy_top_id	512299250
id	SPR020706898
language_de	englisch
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Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. 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author	Zahnd, Guillaume
spellingShingle	Zahnd, Guillaume misc Coronary artery misc Optical coherence tomography misc Interventional imaging misc Thin-cap fibroatheroma misc Contour segmentation misc Dynamic programming misc Preoperative planning Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
authorStr	Zahnd, Guillaume
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topic_title	Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming Coronary artery (dpeaa)DE-He213 Optical coherence tomography (dpeaa)DE-He213 Interventional imaging (dpeaa)DE-He213 Thin-cap fibroatheroma (dpeaa)DE-He213 Contour segmentation (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Preoperative planning (dpeaa)DE-He213
topic	misc Coronary artery misc Optical coherence tomography misc Interventional imaging misc Thin-cap fibroatheroma misc Contour segmentation misc Dynamic programming misc Preoperative planning
topic_unstemmed	misc Coronary artery misc Optical coherence tomography misc Interventional imaging misc Thin-cap fibroatheroma misc Contour segmentation misc Dynamic programming misc Preoperative planning
topic_browse	misc Coronary artery misc Optical coherence tomography misc Interventional imaging misc Thin-cap fibroatheroma misc Contour segmentation misc Dynamic programming misc Preoperative planning
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hierarchy_parent_title	International journal of computer assisted radiology and surgery
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title	Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
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title_full	Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
author_sort	Zahnd, Guillaume
journal	International journal of computer assisted radiology and surgery
journalStr	International journal of computer assisted radiology and surgery
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author_browse	Zahnd, Guillaume Karanasos, Antonios van Soest, Gijs Regar, Evelyn Niessen, Wiro Gijsen, Frank van Walsum, Theo
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author-letter	Zahnd, Guillaume
doi_str_mv	10.1007/s11548-015-1164-7
title_sort	quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
title_auth	Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
abstract	Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. © The Author(s) 2015
abstractGer	Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. © The Author(s) 2015
abstract_unstemmed	Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. Conclusion The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques. © The Author(s) 2015
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container_issue	9
title_short	Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming
url	https://dx.doi.org/10.1007/s11548-015-1164-7
remote_bool	true
author2	Karanasos, Antonios van Soest, Gijs Regar, Evelyn Niessen, Wiro Gijsen, Frank van Walsum, Theo
author2Str	Karanasos, Antonios van Soest, Gijs Regar, Evelyn Niessen, Wiro Gijsen, Frank van Walsum, Theo
ppnlink	512299250
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doi_str	10.1007/s11548-015-1164-7
up_date	2024-07-03T17:44:17.178Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR020706898</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519153401.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2015 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11548-015-1164-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR020706898</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11548-015-1164-7-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="100" ind1="1" ind2=" "><subfield code="a">Zahnd, Guillaume</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objectives Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of %$22\,\pm \,18\,\mu \hbox {m},\hbox { R}\,=\,.73%$) and were similar to inter-observer reproducibility (%$21\,\pm \,19\,\mu \hbox {m}%$, R = .74), while being significantly faster and fully reproducible. 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Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming

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