Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network

Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segme...
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Gespeichert in:

Autor*in:	Yao, Yao [verfasserIn] Chen, Yang [verfasserIn] Gou, Shuiping [verfasserIn] Chen, Shuzhe [verfasserIn] Zhang, Xiangrong [verfasserIn] Tong, Nuo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning

Übergeordnetes Werk:	Enthalten in: Biomedical signal processing and control - Amsterdam [u.a.] : Elsevier, 2006, 83
Übergeordnetes Werk:	volume:83

DOI / URN:	10.1016/j.bspc.2023.104583

Katalog-ID:	ELV009392904

Internformat


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245	1	0	\|a Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network
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520			\|a Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation.
650		4	\|a Adaptive radiation therapy
650		4	\|a Pancreatic cancer
650		4	\|a Tumor segmentation
650		4	\|a Mask R-CNN
650		4	\|a Transfer learning
700	1		\|a Chen, Yang \|e verfasserin \|4 aut
700	1		\|a Gou, Shuiping \|e verfasserin \|4 aut
700	1		\|a Chen, Shuzhe \|e verfasserin \|4 aut
700	1		\|a Zhang, Xiangrong \|e verfasserin \|4 aut
700	1		\|a Tong, Nuo \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Biomedical signal processing and control \|d Amsterdam [u.a.] : Elsevier, 2006 \|g 83 \|h Online-Ressource \|w (DE-627)515537861 \|w (DE-600)2241886-6 \|w (DE-576)261592653 \|x 1746-8108 \|7 nnns
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936	b	k	\|a 44.09 \|j Medizintechnik
936	b	k	\|a 44.32 \|j Medizinische Mathematik \|j medizinische Statistik
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Indexfelder

author_variant	y y yy y c yc s g sg s c sc x z xz n t nt
matchkey_str	article:17468108:2023----::uoemnainfacetcuoimlioaiaesntas
hierarchy_sort_str	2023
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publishDate	2023
allfields	10.1016/j.bspc.2023.104583 doi (DE-627)ELV009392904 (ELSEVIER)S1746-8094(23)00016-2 DE-627 ger DE-627 rda eng 610 DE-600 44.09 bkl 44.32 bkl Yao, Yao verfasserin aut Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation. Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning Chen, Yang verfasserin aut Gou, Shuiping verfasserin aut Chen, Shuzhe verfasserin aut Zhang, Xiangrong verfasserin aut Tong, Nuo verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 83 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:83 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 44.09 Medizintechnik 44.32 Medizinische Mathematik medizinische Statistik AR 83
spelling	10.1016/j.bspc.2023.104583 doi (DE-627)ELV009392904 (ELSEVIER)S1746-8094(23)00016-2 DE-627 ger DE-627 rda eng 610 DE-600 44.09 bkl 44.32 bkl Yao, Yao verfasserin aut Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation. Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning Chen, Yang verfasserin aut Gou, Shuiping verfasserin aut Chen, Shuzhe verfasserin aut Zhang, Xiangrong verfasserin aut Tong, Nuo verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 83 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:83 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 44.09 Medizintechnik 44.32 Medizinische Mathematik medizinische Statistik AR 83
allfields_unstemmed	10.1016/j.bspc.2023.104583 doi (DE-627)ELV009392904 (ELSEVIER)S1746-8094(23)00016-2 DE-627 ger DE-627 rda eng 610 DE-600 44.09 bkl 44.32 bkl Yao, Yao verfasserin aut Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation. Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning Chen, Yang verfasserin aut Gou, Shuiping verfasserin aut Chen, Shuzhe verfasserin aut Zhang, Xiangrong verfasserin aut Tong, Nuo verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 83 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:83 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 44.09 Medizintechnik 44.32 Medizinische Mathematik medizinische Statistik AR 83
allfieldsGer	10.1016/j.bspc.2023.104583 doi (DE-627)ELV009392904 (ELSEVIER)S1746-8094(23)00016-2 DE-627 ger DE-627 rda eng 610 DE-600 44.09 bkl 44.32 bkl Yao, Yao verfasserin aut Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation. Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning Chen, Yang verfasserin aut Gou, Shuiping verfasserin aut Chen, Shuzhe verfasserin aut Zhang, Xiangrong verfasserin aut Tong, Nuo verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 83 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:83 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 44.09 Medizintechnik 44.32 Medizinische Mathematik medizinische Statistik AR 83
allfieldsSound	10.1016/j.bspc.2023.104583 doi (DE-627)ELV009392904 (ELSEVIER)S1746-8094(23)00016-2 DE-627 ger DE-627 rda eng 610 DE-600 44.09 bkl 44.32 bkl Yao, Yao verfasserin aut Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation. Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning Chen, Yang verfasserin aut Gou, Shuiping verfasserin aut Chen, Shuzhe verfasserin aut Zhang, Xiangrong verfasserin aut Tong, Nuo verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 83 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:83 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4393 GBV_ILN_4700 44.09 Medizintechnik 44.32 Medizinische Mathematik medizinische Statistik AR 83
language	English
source	Enthalten in Biomedical signal processing and control 83 volume:83
sourceStr	Enthalten in Biomedical signal processing and control 83 volume:83
format_phy_str_mv	Article
bklname	Medizintechnik Medizinische Mathematik medizinische Statistik
institution	findex.gbv.de
topic_facet	Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning
dewey-raw	610
isfreeaccess_bool	false
container_title	Biomedical signal processing and control
authorswithroles_txt_mv	Yao, Yao @@aut@@ Chen, Yang @@aut@@ Gou, Shuiping @@aut@@ Chen, Shuzhe @@aut@@ Zhang, Xiangrong @@aut@@ Tong, Nuo @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	515537861
dewey-sort	3610
id	ELV009392904
language_de	englisch
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author	Yao, Yao
spellingShingle	Yao, Yao ddc 610 bkl 44.09 bkl 44.32 misc Adaptive radiation therapy misc Pancreatic cancer misc Tumor segmentation misc Mask R-CNN misc Transfer learning Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network
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topic_title	610 DE-600 44.09 bkl 44.32 bkl Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network Adaptive radiation therapy Pancreatic cancer Tumor segmentation Mask R-CNN Transfer learning
topic	ddc 610 bkl 44.09 bkl 44.32 misc Adaptive radiation therapy misc Pancreatic cancer misc Tumor segmentation misc Mask R-CNN misc Transfer learning
topic_unstemmed	ddc 610 bkl 44.09 bkl 44.32 misc Adaptive radiation therapy misc Pancreatic cancer misc Tumor segmentation misc Mask R-CNN misc Transfer learning
topic_browse	ddc 610 bkl 44.09 bkl 44.32 misc Adaptive radiation therapy misc Pancreatic cancer misc Tumor segmentation misc Mask R-CNN misc Transfer learning
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title	Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network
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title_full	Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network
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title_sort	auto-segmentation of pancreatic tumor in multi-modal image using transferred dsmask r-cnn network
title_auth	Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network
abstract	Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation.
abstractGer	Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation.
abstract_unstemmed	Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respectively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and specificity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some literatures. This algorithm can improve the accuracy of pancreatic tumor segmentation.
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title_short	Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network
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author2	Chen, Yang Gou, Shuiping Chen, Shuzhe Zhang, Xiangrong Tong, Nuo
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up_date	2024-07-06T23:00:13.340Z
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Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network

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