3D segmentation of glial cells using fully convolutional networks and k-terminal cut

Lin Yang; Yizhe Zhang; Ian H. Guldner; Siyuan Zhang; Danny Z. Chen

doi:10.1007/978-3-319-46723-8_76

3D segmentation of glial cells using fully convolutional networks and k-terminal cut

Lin Yang, Yizhe Zhang, Ian H. Guldner, Siyuan Zhang, Danny Z. Chen

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

24 Scopus citations

Abstract

Glial cells play an important role in regulating synaptogenesis,development of blood-brain barrier,and brain tumor metastasis. Quantitative analysis of glial cells can offer new insights to many studies. However,the complicated morphology of the protrusions of glial cells and the entangled cell-to-cell network cause significant difficulties to extracting quantitative information in images. In this paper,we present a new method for instance-level segmentation of glial cells in 3D images. First,we obtain accurate voxel-level segmentation by leveraging the recent advances of fully convolutional networks (FCN). Then we develop a kterminal cut algorithm to disentangle the complex cell-to-cell connections. During the cell cutting process,to better capture the nature of glial cells,a shape prior computed based on a multiplicative Voronoi diagram is exploited. Extensive experiments using real 3D images show that our method has superior performance over the state-of-the-art methods.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
Publisher	Springer Verlag
Pages	658-666
Number of pages	9
ISBN (Print)	9783319467221
DOIs	https://doi.org/10.1007/978-3-319-46723-8_76
State	Published - 2016
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9901 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-319-46723-8_76

Cite this

Yang, L., Zhang, Y., Guldner, I. H., Zhang, S., & Chen, D. Z. (2016). 3D segmentation of glial cells using fully convolutional networks and k-terminal cut. In G. Unal, S. Ourselin, L. Joskowicz, M. R. Sabuncu, & W. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 658-666). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_76

3D segmentation of glial cells using fully convolutional networks and k-terminal cut. / Yang, Lin; Zhang, Yizhe; Guldner, Ian H. et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Gozde Unal; Sebastian Ourselin; Leo Joskowicz; Mert R. Sabuncu; William Wells. Springer Verlag, 2016. p. 658-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS).

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

Yang, L, Zhang, Y, Guldner, IH, Zhang, S & Chen, DZ 2016, 3D segmentation of glial cells using fully convolutional networks and k-terminal cut. in G Unal, S Ourselin, L Joskowicz, MR Sabuncu & W Wells (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, Springer Verlag, pp. 658-666. https://doi.org/10.1007/978-3-319-46723-8_76

Yang L, Zhang Y, Guldner IH, Zhang S, Chen DZ. 3D segmentation of glial cells using fully convolutional networks and k-terminal cut. In Unal G, Ourselin S, Joskowicz L, Sabuncu MR, Wells W, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 658-666. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46723-8_76

Yang, Lin ; Zhang, Yizhe ; Guldner, Ian H. et al. / 3D segmentation of glial cells using fully convolutional networks and k-terminal cut. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Gozde Unal ; Sebastian Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; William Wells. Springer Verlag, 2016. pp. 658-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Glial cells play an important role in regulating synaptogenesis,development of blood-brain barrier,and brain tumor metastasis. Quantitative analysis of glial cells can offer new insights to many studies. However,the complicated morphology of the protrusions of glial cells and the entangled cell-to-cell network cause significant difficulties to extracting quantitative information in images. In this paper,we present a new method for instance-level segmentation of glial cells in 3D images. First,we obtain accurate voxel-level segmentation by leveraging the recent advances of fully convolutional networks (FCN). Then we develop a kterminal cut algorithm to disentangle the complex cell-to-cell connections. During the cell cutting process,to better capture the nature of glial cells,a shape prior computed based on a multiplicative Voronoi diagram is exploited. Extensive experiments using real 3D images show that our method has superior performance over the state-of-the-art methods.",

author = "Lin Yang and Yizhe Zhang and Guldner, {Ian H.} and Siyuan Zhang and Chen, {Danny Z.}",

note = "Funding Information: This research was supported in part by NSF Grants CCF-1217906 and CCF-1617735, and by NIH Grant 5R01CA194697-02. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",

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AU - Guldner, Ian H.

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AU - Chen, Danny Z.

N1 - Funding Information: This research was supported in part by NSF Grants CCF-1217906 and CCF-1617735, and by NIH Grant 5R01CA194697-02. Publisher Copyright: © Springer International Publishing AG 2016.

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Y1 - 2016

N2 - Glial cells play an important role in regulating synaptogenesis,development of blood-brain barrier,and brain tumor metastasis. Quantitative analysis of glial cells can offer new insights to many studies. However,the complicated morphology of the protrusions of glial cells and the entangled cell-to-cell network cause significant difficulties to extracting quantitative information in images. In this paper,we present a new method for instance-level segmentation of glial cells in 3D images. First,we obtain accurate voxel-level segmentation by leveraging the recent advances of fully convolutional networks (FCN). Then we develop a kterminal cut algorithm to disentangle the complex cell-to-cell connections. During the cell cutting process,to better capture the nature of glial cells,a shape prior computed based on a multiplicative Voronoi diagram is exploited. Extensive experiments using real 3D images show that our method has superior performance over the state-of-the-art methods.

AB - Glial cells play an important role in regulating synaptogenesis,development of blood-brain barrier,and brain tumor metastasis. Quantitative analysis of glial cells can offer new insights to many studies. However,the complicated morphology of the protrusions of glial cells and the entangled cell-to-cell network cause significant difficulties to extracting quantitative information in images. In this paper,we present a new method for instance-level segmentation of glial cells in 3D images. First,we obtain accurate voxel-level segmentation by leveraging the recent advances of fully convolutional networks (FCN). Then we develop a kterminal cut algorithm to disentangle the complex cell-to-cell connections. During the cell cutting process,to better capture the nature of glial cells,a shape prior computed based on a multiplicative Voronoi diagram is exploited. Extensive experiments using real 3D images show that our method has superior performance over the state-of-the-art methods.

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3D segmentation of glial cells using fully convolutional networks and k-terminal cut

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