Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning

Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) pati...
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Autor*in:	Ermiş, Ekin [verfasserIn] Jungo, Alain [verfasserIn] Poel, Robert [verfasserIn] Blatti-Moreno, Marcela [verfasserIn] Meier, Raphael [verfasserIn] Knecht, Urspeter [verfasserIn] Aebersold, Daniel M. [verfasserIn] Fix, Michael K. [verfasserIn] Manser, Peter [verfasserIn] Reyes, Mauricio [verfasserIn] Herrmann, Evelyn [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Glioblastoma Automatic segmentation Deep learning Target definition MRI

Übergeordnetes Werk:	Enthalten in: Radiation oncology - London : BioMed Central, 2006, 15(2020), 1 vom: 06. Mai
Übergeordnetes Werk:	volume:15 ; year:2020 ; number:1 ; day:06 ; month:05

Links:	Volltext

DOI / URN:	10.1186/s13014-020-01553-z

Katalog-ID:	SPR039625710

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520			\|a Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar.
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allfields	10.1186/s13014-020-01553-z doi (DE-627)SPR039625710 (SPR)s13014-020-01553-z-e DE-627 ger DE-627 rakwb eng 610 ASE Ermiş, Ekin verfasserin aut Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar. Glioblastoma (dpeaa)DE-He213 Automatic segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Target definition (dpeaa)DE-He213 MRI (dpeaa)DE-He213 Jungo, Alain verfasserin aut Poel, Robert verfasserin aut Blatti-Moreno, Marcela verfasserin aut Meier, Raphael verfasserin aut Knecht, Urspeter verfasserin aut Aebersold, Daniel M. verfasserin aut Fix, Michael K. verfasserin aut Manser, Peter verfasserin aut Reyes, Mauricio verfasserin aut Herrmann, Evelyn verfasserin aut Enthalten in Radiation oncology London : BioMed Central, 2006 15(2020), 1 vom: 06. Mai (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:15 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s13014-020-01553-z 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_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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2020 1 06 05
spelling	10.1186/s13014-020-01553-z doi (DE-627)SPR039625710 (SPR)s13014-020-01553-z-e DE-627 ger DE-627 rakwb eng 610 ASE Ermiş, Ekin verfasserin aut Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar. Glioblastoma (dpeaa)DE-He213 Automatic segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Target definition (dpeaa)DE-He213 MRI (dpeaa)DE-He213 Jungo, Alain verfasserin aut Poel, Robert verfasserin aut Blatti-Moreno, Marcela verfasserin aut Meier, Raphael verfasserin aut Knecht, Urspeter verfasserin aut Aebersold, Daniel M. verfasserin aut Fix, Michael K. verfasserin aut Manser, Peter verfasserin aut Reyes, Mauricio verfasserin aut Herrmann, Evelyn verfasserin aut Enthalten in Radiation oncology London : BioMed Central, 2006 15(2020), 1 vom: 06. Mai (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:15 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s13014-020-01553-z 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_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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2020 1 06 05
allfields_unstemmed	10.1186/s13014-020-01553-z doi (DE-627)SPR039625710 (SPR)s13014-020-01553-z-e DE-627 ger DE-627 rakwb eng 610 ASE Ermiş, Ekin verfasserin aut Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar. Glioblastoma (dpeaa)DE-He213 Automatic segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Target definition (dpeaa)DE-He213 MRI (dpeaa)DE-He213 Jungo, Alain verfasserin aut Poel, Robert verfasserin aut Blatti-Moreno, Marcela verfasserin aut Meier, Raphael verfasserin aut Knecht, Urspeter verfasserin aut Aebersold, Daniel M. verfasserin aut Fix, Michael K. verfasserin aut Manser, Peter verfasserin aut Reyes, Mauricio verfasserin aut Herrmann, Evelyn verfasserin aut Enthalten in Radiation oncology London : BioMed Central, 2006 15(2020), 1 vom: 06. Mai (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:15 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s13014-020-01553-z 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_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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2020 1 06 05
allfieldsGer	10.1186/s13014-020-01553-z doi (DE-627)SPR039625710 (SPR)s13014-020-01553-z-e DE-627 ger DE-627 rakwb eng 610 ASE Ermiş, Ekin verfasserin aut Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar. Glioblastoma (dpeaa)DE-He213 Automatic segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Target definition (dpeaa)DE-He213 MRI (dpeaa)DE-He213 Jungo, Alain verfasserin aut Poel, Robert verfasserin aut Blatti-Moreno, Marcela verfasserin aut Meier, Raphael verfasserin aut Knecht, Urspeter verfasserin aut Aebersold, Daniel M. verfasserin aut Fix, Michael K. verfasserin aut Manser, Peter verfasserin aut Reyes, Mauricio verfasserin aut Herrmann, Evelyn verfasserin aut Enthalten in Radiation oncology London : BioMed Central, 2006 15(2020), 1 vom: 06. Mai (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:15 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s13014-020-01553-z 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_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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2020 1 06 05
allfieldsSound	10.1186/s13014-020-01553-z doi (DE-627)SPR039625710 (SPR)s13014-020-01553-z-e DE-627 ger DE-627 rakwb eng 610 ASE Ermiş, Ekin verfasserin aut Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar. Glioblastoma (dpeaa)DE-He213 Automatic segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Target definition (dpeaa)DE-He213 MRI (dpeaa)DE-He213 Jungo, Alain verfasserin aut Poel, Robert verfasserin aut Blatti-Moreno, Marcela verfasserin aut Meier, Raphael verfasserin aut Knecht, Urspeter verfasserin aut Aebersold, Daniel M. verfasserin aut Fix, Michael K. verfasserin aut Manser, Peter verfasserin aut Reyes, Mauricio verfasserin aut Herrmann, Evelyn verfasserin aut Enthalten in Radiation oncology London : BioMed Central, 2006 15(2020), 1 vom: 06. Mai (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:15 year:2020 number:1 day:06 month:05 https://dx.doi.org/10.1186/s13014-020-01553-z 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_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_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2020 1 06 05
language	English
source	Enthalten in Radiation oncology 15(2020), 1 vom: 06. Mai volume:15 year:2020 number:1 day:06 month:05
sourceStr	Enthalten in Radiation oncology 15(2020), 1 vom: 06. Mai volume:15 year:2020 number:1 day:06 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Glioblastoma Automatic segmentation Deep learning Target definition MRI
dewey-raw	610
isfreeaccess_bool	true
container_title	Radiation oncology
authorswithroles_txt_mv	Ermiş, Ekin @@aut@@ Jungo, Alain @@aut@@ Poel, Robert @@aut@@ Blatti-Moreno, Marcela @@aut@@ Meier, Raphael @@aut@@ Knecht, Urspeter @@aut@@ Aebersold, Daniel M. @@aut@@ Fix, Michael K. @@aut@@ Manser, Peter @@aut@@ Reyes, Mauricio @@aut@@ Herrmann, Evelyn @@aut@@
publishDateDaySort_date	2020-05-06T00:00:00Z
hierarchy_top_id	508725739
dewey-sort	3610
id	SPR039625710
language_de	englisch
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author	Ermiş, Ekin
spellingShingle	Ermiş, Ekin ddc 610 misc Glioblastoma misc Automatic segmentation misc Deep learning misc Target definition misc MRI Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning
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topic_title	610 ASE Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning Glioblastoma (dpeaa)DE-He213 Automatic segmentation (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Target definition (dpeaa)DE-He213 MRI (dpeaa)DE-He213
topic	ddc 610 misc Glioblastoma misc Automatic segmentation misc Deep learning misc Target definition misc MRI
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title	Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning
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title_full	Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning
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author_browse	Ermiş, Ekin Jungo, Alain Poel, Robert Blatti-Moreno, Marcela Meier, Raphael Knecht, Urspeter Aebersold, Daniel M. Fix, Michael K. Manser, Peter Reyes, Mauricio Herrmann, Evelyn
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title_sort	fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning
title_auth	Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning
abstract	Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar.
abstractGer	Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar.
abstract_unstemmed	Background Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. Methods Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. Results Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. Conclusions The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar.
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title_short	Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning
url	https://dx.doi.org/10.1186/s13014-020-01553-z
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author2	Jungo, Alain Poel, Robert Blatti-Moreno, Marcela Meier, Raphael Knecht, Urspeter Aebersold, Daniel M. Fix, Michael K. Manser, Peter Reyes, Mauricio Herrmann, Evelyn
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