Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks

This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yang, Xulei [verfasserIn] Tang, Wai Teng [verfasserIn] Tjio, Gabriel [verfasserIn] Yeo, Si Yong [verfasserIn] Su, Yi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC)

Übergeordnetes Werk:	Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 396, Seite 514-521
Übergeordnetes Werk:	volume:396 ; pages:514-521

DOI / URN:	10.1016/j.neucom.2018.10.105

Katalog-ID:	ELV004125134

Internformat


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245	1	0	\|a Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks
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520			\|a This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model.
650		4	\|a Automatic graphical prescription
650		4	\|a Brain landmark detection
650		4	\|a Multi-task learning
650		4	\|a Deep neural networks
650		4	\|a anterior commissures (AC) and posterior commissures (PC)
700	1		\|a Tang, Wai Teng \|e verfasserin \|4 aut
700	1		\|a Tjio, Gabriel \|e verfasserin \|4 aut
700	1		\|a Yeo, Si Yong \|e verfasserin \|4 aut
700	1		\|a Su, Yi \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Neurocomputing \|d Amsterdam : Elsevier, 1989 \|g 396, Seite 514-521 \|h Online-Ressource \|w (DE-627)271176008 \|w (DE-600)1479006-3 \|w (DE-576)078412358 \|x 1872-8286 \|7 nnns
773	1	8	\|g volume:396 \|g pages:514-521
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936	b	k	\|a 54.72 \|j Künstliche Intelligenz
951			\|a AR
952			\|d 396 \|h 514-521

Indexfelder

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matchkey_str	article:18728286:2019----::uoaidtcinfntmcladakibanrcnigsnmli
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bklnumber	54.72
publishDate	2019
allfields	10.1016/j.neucom.2018.10.105 doi (DE-627)ELV004125134 (ELSEVIER)S0925-2312(19)30487-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Yang, Xulei verfasserin (orcid)0000-0002-7002-4564 aut Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model. Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC) Tang, Wai Teng verfasserin aut Tjio, Gabriel verfasserin aut Yeo, Si Yong verfasserin aut Su, Yi verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 396, Seite 514-521 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:396 pages:514-521 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_63 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 396 514-521
spelling	10.1016/j.neucom.2018.10.105 doi (DE-627)ELV004125134 (ELSEVIER)S0925-2312(19)30487-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Yang, Xulei verfasserin (orcid)0000-0002-7002-4564 aut Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model. Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC) Tang, Wai Teng verfasserin aut Tjio, Gabriel verfasserin aut Yeo, Si Yong verfasserin aut Su, Yi verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 396, Seite 514-521 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:396 pages:514-521 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_63 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 396 514-521
allfields_unstemmed	10.1016/j.neucom.2018.10.105 doi (DE-627)ELV004125134 (ELSEVIER)S0925-2312(19)30487-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Yang, Xulei verfasserin (orcid)0000-0002-7002-4564 aut Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model. Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC) Tang, Wai Teng verfasserin aut Tjio, Gabriel verfasserin aut Yeo, Si Yong verfasserin aut Su, Yi verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 396, Seite 514-521 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:396 pages:514-521 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_63 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 396 514-521
allfieldsGer	10.1016/j.neucom.2018.10.105 doi (DE-627)ELV004125134 (ELSEVIER)S0925-2312(19)30487-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Yang, Xulei verfasserin (orcid)0000-0002-7002-4564 aut Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model. Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC) Tang, Wai Teng verfasserin aut Tjio, Gabriel verfasserin aut Yeo, Si Yong verfasserin aut Su, Yi verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 396, Seite 514-521 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:396 pages:514-521 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_63 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 396 514-521
allfieldsSound	10.1016/j.neucom.2018.10.105 doi (DE-627)ELV004125134 (ELSEVIER)S0925-2312(19)30487-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Yang, Xulei verfasserin (orcid)0000-0002-7002-4564 aut Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model. Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC) Tang, Wai Teng verfasserin aut Tjio, Gabriel verfasserin aut Yeo, Si Yong verfasserin aut Su, Yi verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 396, Seite 514-521 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:396 pages:514-521 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_63 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_2008 GBV_ILN_2010 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_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 54.72 Künstliche Intelligenz AR 396 514-521
language	English
source	Enthalten in Neurocomputing 396, Seite 514-521 volume:396 pages:514-521
sourceStr	Enthalten in Neurocomputing 396, Seite 514-521 volume:396 pages:514-521
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC)
dewey-raw	610
isfreeaccess_bool	false
container_title	Neurocomputing
authorswithroles_txt_mv	Yang, Xulei @@aut@@ Tang, Wai Teng @@aut@@ Tjio, Gabriel @@aut@@ Yeo, Si Yong @@aut@@ Su, Yi @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	271176008
dewey-sort	3610
id	ELV004125134
language_de	englisch
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author	Yang, Xulei
spellingShingle	Yang, Xulei ddc 610 bkl 54.72 misc Automatic graphical prescription misc Brain landmark detection misc Multi-task learning misc Deep neural networks misc anterior commissures (AC) and posterior commissures (PC) Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks
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topic_title	610 DE-600 54.72 bkl Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks Automatic graphical prescription Brain landmark detection Multi-task learning Deep neural networks anterior commissures (AC) and posterior commissures (PC)
topic	ddc 610 bkl 54.72 misc Automatic graphical prescription misc Brain landmark detection misc Multi-task learning misc Deep neural networks misc anterior commissures (AC) and posterior commissures (PC)
topic_unstemmed	ddc 610 bkl 54.72 misc Automatic graphical prescription misc Brain landmark detection misc Multi-task learning misc Deep neural networks misc anterior commissures (AC) and posterior commissures (PC)
topic_browse	ddc 610 bkl 54.72 misc Automatic graphical prescription misc Brain landmark detection misc Multi-task learning misc Deep neural networks misc anterior commissures (AC) and posterior commissures (PC)
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title	Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks
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title_full	Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks
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title_sort	automatic detection of anatomical landmarks in brain mr scanning using multi-task deep neural networks
title_auth	Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks
abstract	This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model.
abstractGer	This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model.
abstract_unstemmed	This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model.
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title_short	Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks
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author2	Tang, Wai Teng Tjio, Gabriel Yeo, Si Yong Su, Yi
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doi_str	10.1016/j.neucom.2018.10.105
up_date	2024-07-06T21:56:05.990Z
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Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks

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