Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation

Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this pa...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Tang, Xianlun [verfasserIn] Zhong, Bing [verfasserIn] Peng, Jiangping [verfasserIn] Hao, Bohui [verfasserIn] Li, Jie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 93
Übergeordnetes Werk:	volume:93

DOI / URN:	10.1016/j.asoc.2020.106353

Katalog-ID:	ELV004438795

Internformat


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520			\|a Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels.
650		4	\|a Multi-scale
650		4	\|a Asymmetric cascade convolution
650		4	\|a Channel independence
650		4	\|a Spatial attention mechanism
650		4	\|a Retinal vessels
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700	1		\|a Hao, Bohui \|e verfasserin \|4 aut
700	1		\|a Li, Jie \|e verfasserin \|4 aut
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Indexfelder

author_variant	x t xt b z bz j p jp b h bh j l jl
matchkey_str	article:15684946:2020----::utsaehneipracsrignsailtetomcaimor
hierarchy_sort_str	2020
bklnumber	54.00
publishDate	2020
allfields	10.1016/j.asoc.2020.106353 doi (DE-627)ELV004438795 (ELSEVIER)S1568-4946(20)30293-3 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tang, Xianlun verfasserin aut Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels. Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels Zhong, Bing verfasserin aut Peng, Jiangping verfasserin aut Hao, Bohui verfasserin aut Li, Jie verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 93 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:93 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 93
spelling	10.1016/j.asoc.2020.106353 doi (DE-627)ELV004438795 (ELSEVIER)S1568-4946(20)30293-3 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tang, Xianlun verfasserin aut Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels. Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels Zhong, Bing verfasserin aut Peng, Jiangping verfasserin aut Hao, Bohui verfasserin aut Li, Jie verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 93 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:93 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 93
allfields_unstemmed	10.1016/j.asoc.2020.106353 doi (DE-627)ELV004438795 (ELSEVIER)S1568-4946(20)30293-3 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tang, Xianlun verfasserin aut Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels. Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels Zhong, Bing verfasserin aut Peng, Jiangping verfasserin aut Hao, Bohui verfasserin aut Li, Jie verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 93 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:93 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 93
allfieldsGer	10.1016/j.asoc.2020.106353 doi (DE-627)ELV004438795 (ELSEVIER)S1568-4946(20)30293-3 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tang, Xianlun verfasserin aut Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels. Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels Zhong, Bing verfasserin aut Peng, Jiangping verfasserin aut Hao, Bohui verfasserin aut Li, Jie verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 93 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:93 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 93
allfieldsSound	10.1016/j.asoc.2020.106353 doi (DE-627)ELV004438795 (ELSEVIER)S1568-4946(20)30293-3 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tang, Xianlun verfasserin aut Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels. Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels Zhong, Bing verfasserin aut Peng, Jiangping verfasserin aut Hao, Bohui verfasserin aut Li, Jie verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 93 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:93 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 93
language	English
source	Enthalten in Applied soft computing 93 volume:93
sourceStr	Enthalten in Applied soft computing 93 volume:93
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Tang, Xianlun @@aut@@ Zhong, Bing @@aut@@ Peng, Jiangping @@aut@@ Hao, Bohui @@aut@@ Li, Jie @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV004438795
language_de	englisch
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author	Tang, Xianlun
spellingShingle	Tang, Xianlun ddc 004 bkl 54.00 misc Multi-scale misc Asymmetric cascade convolution misc Channel independence misc Spatial attention mechanism misc Retinal vessels Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation
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topic_title	004 DE-600 54.00 bkl Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation Multi-scale Asymmetric cascade convolution Channel independence Spatial attention mechanism Retinal vessels
topic	ddc 004 bkl 54.00 misc Multi-scale misc Asymmetric cascade convolution misc Channel independence misc Spatial attention mechanism misc Retinal vessels
topic_unstemmed	ddc 004 bkl 54.00 misc Multi-scale misc Asymmetric cascade convolution misc Channel independence misc Spatial attention mechanism misc Retinal vessels
topic_browse	ddc 004 bkl 54.00 misc Multi-scale misc Asymmetric cascade convolution misc Channel independence misc Spatial attention mechanism misc Retinal vessels
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title	Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation
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title_full	Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation
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author_browse	Tang, Xianlun Zhong, Bing Peng, Jiangping Hao, Bohui Li, Jie
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title_sort	multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation
title_auth	Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation
abstract	Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels.
abstractGer	Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels.
abstract_unstemmed	Retinal Vessels segmentation is an important procedure for detecting and diagnosing a variety of pathological diseases. However, the inherent complex properties around the disc make it challenging to improve the segmenting accuracy of capillaries and the retinal blood vessels at the ends. In this paper, we proposed a multi-scale channel importance sorting and important spatial information positioning (MSCS) encoder–decoder for segmentation in Retinal Vessels. Firstly, the fully convolutional encoder–decoder is formed to implement a series of linear and non-linear transformation and achieve end-to-end segmentation tasks. Then, the channel importance sorting module is employed to suppress useless feature responses during the process of encoding and to identify effective channels, whose information is utilized to recognize capillaries and the retinal vessels at the ends. Finally, in the decoding stage, the spatial attention mechanism module is designed to extract the positioning information of multi-scale feature maps. The spatial information of retinal vessels is collected to better locate the position of the vessels. In addition, aiming at taking fully advantage of the network, the multi-scale asymmetric cascade convolution module is proposed to reduce the parameters of the model and increase the operation rate. Experimental results on DRIVE, STARE datasets indicate that the proposed method outperforms other state-of-the-art strategies. This system, as demonstrated, can greatly decrease false positive rate of the blood vessels at the ends and enhance the sharpness of retinal vessels.
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title_short	Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation
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author2	Zhong, Bing Peng, Jiangping Hao, Bohui Li, Jie
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doi_str	10.1016/j.asoc.2020.106353
up_date	2024-07-06T22:59:34.223Z
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Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation

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