Brain image labeling using multi-atlas guided 3D fully convolutional networks

Longwei Fang; Lichi Zhang; Dong Nie; Xiaohuan Cao; Khosro Bahrami; Huiguang He; Dinggang Shen

doi:10.1007/978-3-319-67434-6_2

Brain image labeling using multi-atlas guided 3D fully convolutional networks

Longwei Fang, Lichi Zhang, Dong Nie, Xiaohuan Cao, Khosro Bahrami, Huiguang He, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

Automatic labeling of anatomical structures in brain images plays an important role in neuroimaging analysis. Among all methods, multi-atlas based segmentation methods are widely used, due to their robustness in propagating prior label information. However, non-linear registration is always needed, which is time-consuming. Alternatively, the patch-based methods have been proposed to relax the requirement of image registration, but the labeling is often determined independently by the target image information, without getting direct assistance from the atlases. To address these limitations, in this paper, we propose a multi-atlas guided 3D fully convolutional networks (FCN) for brain image labeling. Specifically, multi-atlas based guidance is incorporated during the network learning. Based on this, the discriminative of the FCN is boosted, which eventually contribute to accurate prediction. Experiments show that the use of multi-atlas guidance improves the brain labeling performance.

Original language	English
Title of host publication	Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings
Editors	Yiqiang Zhan, Wenjia Bai, Guorong Wu, Pierrick Coupe, Brent C. Munsell, Gerard Sanroma
Publisher	Springer Verlag
Pages	12-19
Number of pages	8
ISBN (Print)	9783319674339
DOIs	https://doi.org/10.1007/978-3-319-67434-6_2
Publication status	Published - 2017
Event	3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 2017 Sept 14 → 2017 Sept 14

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10530 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	17/9/14 → 17/9/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-67434-6_2

Cite this

Fang, L., Zhang, L., Nie, D., Cao, X., Bahrami, K., He, H., & Shen, D. (2017). Brain image labeling using multi-atlas guided 3D fully convolutional networks. In Y. Zhan, W. Bai, G. Wu, P. Coupe, B. C. Munsell, & G. Sanroma (Eds.), Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 12-19). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10530 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67434-6_2

Brain image labeling using multi-atlas guided 3D fully convolutional networks. / Fang, Longwei; Zhang, Lichi; Nie, Dong et al.
Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. ed. / Yiqiang Zhan; Wenjia Bai; Guorong Wu; Pierrick Coupe; Brent C. Munsell; Gerard Sanroma. Springer Verlag, 2017. p. 12-19 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10530 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fang, L, Zhang, L, Nie, D, Cao, X, Bahrami, K, He, H & Shen, D 2017, Brain image labeling using multi-atlas guided 3D fully convolutional networks. in Y Zhan, W Bai, G Wu, P Coupe, BC Munsell & G Sanroma (eds), Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10530 LNCS, Springer Verlag, pp. 12-19, 3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/14. https://doi.org/10.1007/978-3-319-67434-6_2

Fang L, Zhang L, Nie D, Cao X, Bahrami K, He H et al. Brain image labeling using multi-atlas guided 3D fully convolutional networks. In Zhan Y, Bai W, Wu G, Coupe P, Munsell BC, Sanroma G, editors, Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag. 2017. p. 12-19. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67434-6_2

Fang, Longwei ; Zhang, Lichi ; Nie, Dong et al. / Brain image labeling using multi-atlas guided 3D fully convolutional networks. Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings. editor / Yiqiang Zhan ; Wenjia Bai ; Guorong Wu ; Pierrick Coupe ; Brent C. Munsell ; Gerard Sanroma. Springer Verlag, 2017. pp. 12-19 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Automatic labeling of anatomical structures in brain images plays an important role in neuroimaging analysis. Among all methods, multi-atlas based segmentation methods are widely used, due to their robustness in propagating prior label information. However, non-linear registration is always needed, which is time-consuming. Alternatively, the patch-based methods have been proposed to relax the requirement of image registration, but the labeling is often determined independently by the target image information, without getting direct assistance from the atlases. To address these limitations, in this paper, we propose a multi-atlas guided 3D fully convolutional networks (FCN) for brain image labeling. Specifically, multi-atlas based guidance is incorporated during the network learning. Based on this, the discriminative of the FCN is boosted, which eventually contribute to accurate prediction. Experiments show that the use of multi-atlas guidance improves the brain labeling performance.",

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AU - He, Huiguang

AU - Shen, Dinggang

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AB - Automatic labeling of anatomical structures in brain images plays an important role in neuroimaging analysis. Among all methods, multi-atlas based segmentation methods are widely used, due to their robustness in propagating prior label information. However, non-linear registration is always needed, which is time-consuming. Alternatively, the patch-based methods have been proposed to relax the requirement of image registration, but the labeling is often determined independently by the target image information, without getting direct assistance from the atlases. To address these limitations, in this paper, we propose a multi-atlas guided 3D fully convolutional networks (FCN) for brain image labeling. Specifically, multi-atlas based guidance is incorporated during the network learning. Based on this, the discriminative of the FCN is boosted, which eventually contribute to accurate prediction. Experiments show that the use of multi-atlas guidance improves the brain labeling performance.

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Brain image labeling using multi-atlas guided 3D fully convolutional networks

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