Semi-automatic segmentation of pelvic bone tumors: Usability testing

In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), ortho...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Luciano Vidal [verfasserIn] Vincent Biscaccianti [verfasserIn] Henri Fragnaud [verfasserIn] Jean-Yves Hascoët [verfasserIn] Vincent Crenn [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor

Übergeordnetes Werk:	In: Annals of 3D Printed Medicine - Elsevier, 2021, 9(2023), Seite 100098-
Übergeordnetes Werk:	volume:9 ; year:2023 ; pages:100098-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.stlm.2022.100098

Katalog-ID:	DOAJ020799160

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allfields	10.1016/j.stlm.2022.100098 doi (DE-627)DOAJ020799160 (DE-599)DOAJ433d9b3f7f9544ebb16ca6c286e86812 DE-627 ger DE-627 rakwb eng R855-855.5 Luciano Vidal verfasserin aut Semi-automatic segmentation of pelvic bone tumors: Usability testing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes. Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor Medical technology Vincent Biscaccianti verfasserin aut Henri Fragnaud verfasserin aut Jean-Yves Hascoët verfasserin aut Vincent Crenn verfasserin aut In Annals of 3D Printed Medicine Elsevier, 2021 9(2023), Seite 100098- (DE-627)1759893900 26669641 nnns volume:9 year:2023 pages:100098- https://doi.org/10.1016/j.stlm.2022.100098 kostenfrei https://doaj.org/article/433d9b3f7f9544ebb16ca6c286e86812 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666964122000522 kostenfrei https://doaj.org/toc/2666-9641 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 9 2023 100098-
spelling	10.1016/j.stlm.2022.100098 doi (DE-627)DOAJ020799160 (DE-599)DOAJ433d9b3f7f9544ebb16ca6c286e86812 DE-627 ger DE-627 rakwb eng R855-855.5 Luciano Vidal verfasserin aut Semi-automatic segmentation of pelvic bone tumors: Usability testing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes. Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor Medical technology Vincent Biscaccianti verfasserin aut Henri Fragnaud verfasserin aut Jean-Yves Hascoët verfasserin aut Vincent Crenn verfasserin aut In Annals of 3D Printed Medicine Elsevier, 2021 9(2023), Seite 100098- (DE-627)1759893900 26669641 nnns volume:9 year:2023 pages:100098- https://doi.org/10.1016/j.stlm.2022.100098 kostenfrei https://doaj.org/article/433d9b3f7f9544ebb16ca6c286e86812 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666964122000522 kostenfrei https://doaj.org/toc/2666-9641 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 9 2023 100098-
allfields_unstemmed	10.1016/j.stlm.2022.100098 doi (DE-627)DOAJ020799160 (DE-599)DOAJ433d9b3f7f9544ebb16ca6c286e86812 DE-627 ger DE-627 rakwb eng R855-855.5 Luciano Vidal verfasserin aut Semi-automatic segmentation of pelvic bone tumors: Usability testing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes. Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor Medical technology Vincent Biscaccianti verfasserin aut Henri Fragnaud verfasserin aut Jean-Yves Hascoët verfasserin aut Vincent Crenn verfasserin aut In Annals of 3D Printed Medicine Elsevier, 2021 9(2023), Seite 100098- (DE-627)1759893900 26669641 nnns volume:9 year:2023 pages:100098- https://doi.org/10.1016/j.stlm.2022.100098 kostenfrei https://doaj.org/article/433d9b3f7f9544ebb16ca6c286e86812 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666964122000522 kostenfrei https://doaj.org/toc/2666-9641 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 9 2023 100098-
allfieldsGer	10.1016/j.stlm.2022.100098 doi (DE-627)DOAJ020799160 (DE-599)DOAJ433d9b3f7f9544ebb16ca6c286e86812 DE-627 ger DE-627 rakwb eng R855-855.5 Luciano Vidal verfasserin aut Semi-automatic segmentation of pelvic bone tumors: Usability testing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes. Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor Medical technology Vincent Biscaccianti verfasserin aut Henri Fragnaud verfasserin aut Jean-Yves Hascoët verfasserin aut Vincent Crenn verfasserin aut In Annals of 3D Printed Medicine Elsevier, 2021 9(2023), Seite 100098- (DE-627)1759893900 26669641 nnns volume:9 year:2023 pages:100098- https://doi.org/10.1016/j.stlm.2022.100098 kostenfrei https://doaj.org/article/433d9b3f7f9544ebb16ca6c286e86812 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666964122000522 kostenfrei https://doaj.org/toc/2666-9641 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 9 2023 100098-
allfieldsSound	10.1016/j.stlm.2022.100098 doi (DE-627)DOAJ020799160 (DE-599)DOAJ433d9b3f7f9544ebb16ca6c286e86812 DE-627 ger DE-627 rakwb eng R855-855.5 Luciano Vidal verfasserin aut Semi-automatic segmentation of pelvic bone tumors: Usability testing 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes. Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor Medical technology Vincent Biscaccianti verfasserin aut Henri Fragnaud verfasserin aut Jean-Yves Hascoët verfasserin aut Vincent Crenn verfasserin aut In Annals of 3D Printed Medicine Elsevier, 2021 9(2023), Seite 100098- (DE-627)1759893900 26669641 nnns volume:9 year:2023 pages:100098- https://doi.org/10.1016/j.stlm.2022.100098 kostenfrei https://doaj.org/article/433d9b3f7f9544ebb16ca6c286e86812 kostenfrei http://www.sciencedirect.com/science/article/pii/S2666964122000522 kostenfrei https://doaj.org/toc/2666-9641 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 9 2023 100098-
language	English
source	In Annals of 3D Printed Medicine 9(2023), Seite 100098- volume:9 year:2023 pages:100098-
sourceStr	In Annals of 3D Printed Medicine 9(2023), Seite 100098- volume:9 year:2023 pages:100098-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor Medical technology
isfreeaccess_bool	true
container_title	Annals of 3D Printed Medicine
authorswithroles_txt_mv	Luciano Vidal @@aut@@ Vincent Biscaccianti @@aut@@ Henri Fragnaud @@aut@@ Jean-Yves Hascoët @@aut@@ Vincent Crenn @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	1759893900
id	DOAJ020799160
language_de	englisch
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callnumber-first	R - Medicine
author	Luciano Vidal
spellingShingle	Luciano Vidal misc R855-855.5 misc Surgical cutting guides misc Digital chain misc Patient-specific instruments misc Reconstruction surgery misc 3D printing misc Pelvic bone tumor misc Medical technology Semi-automatic segmentation of pelvic bone tumors: Usability testing
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topic_title	R855-855.5 Semi-automatic segmentation of pelvic bone tumors: Usability testing Surgical cutting guides Digital chain Patient-specific instruments Reconstruction surgery 3D printing Pelvic bone tumor
topic	misc R855-855.5 misc Surgical cutting guides misc Digital chain misc Patient-specific instruments misc Reconstruction surgery misc 3D printing misc Pelvic bone tumor misc Medical technology
topic_unstemmed	misc R855-855.5 misc Surgical cutting guides misc Digital chain misc Patient-specific instruments misc Reconstruction surgery misc 3D printing misc Pelvic bone tumor misc Medical technology
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title	Semi-automatic segmentation of pelvic bone tumors: Usability testing
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title_full	Semi-automatic segmentation of pelvic bone tumors: Usability testing
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title_sort	semi-automatic segmentation of pelvic bone tumors: usability testing
callnumber	R855-855.5
title_auth	Semi-automatic segmentation of pelvic bone tumors: Usability testing
abstract	In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes.
abstractGer	In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes.
abstract_unstemmed	In this study, we focus on studying inter-user variability of a semi-automatic image processing pipeline used for 3D model production and subsequent PSI design in pelvic tumor resection. Six retrospective cases of pelvic bone tumors were segmented. Three different users (trained engineer (TE), orthopedic student (OS), expert orthopedic surgeon (OE)) performed multimodal semi-automatic segmentation on registered CT and MRI sequences. Inter-user variability was evaluated on the tumor models using the symmetrical Hausdorff distances and the DICE similarity coefficient. The mean symmetrical Hausdorff distance was 1.1 mm between TE and OS, 3.8 mm between TE and OE, and 3.6 mm between OS and OE. The mean values for the DICE similarity coefficient were 0.91 between TE and OS, 0.79 between TE and OE, and 0.82 between OS and OE. In patient 5, the DICE coefficient between TE and OE, and OS and OE dropped to 0.18 and 0.24 respectively. This study suggests that the inter-user variability between two segmentations of the same tumor cannot be overlooked, especially in complex pelvic tumors: OE expertise seems mandatory for tumor segmentation validation. The collaboration between engineers and clinicians also seems crucial for developing this type of pipeline for patient-specific instruments design purposes.
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title_short	Semi-automatic segmentation of pelvic bone tumors: Usability testing
url	https://doi.org/10.1016/j.stlm.2022.100098 https://doaj.org/article/433d9b3f7f9544ebb16ca6c286e86812 http://www.sciencedirect.com/science/article/pii/S2666964122000522 https://doaj.org/toc/2666-9641
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up_date	2024-07-03T17:04:11.715Z
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Semi-automatic segmentation of pelvic bone tumors: Usability testing

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