GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves

Sarah Parisot; Ben Glocker; Markus D. Schirmer; Daniel Rueckert

doi:10.1007/978-3-319-46720-7_18

GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves

Sarah Parisot, Ben Glocker, Markus D. Schirmer, Daniel Rueckert

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

10 Scopus citations

Abstract

Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven parcellation is a natural approach,but suffers from the lack of reliability of connectivity data. Combining modalities in the parcellation task has the potential to yield more robust parcellations,yet hasn’t been explored much. In this paper,we propose a graphbased multi-modal parcellation method that iteratively computes a set of modality specific parcellations and merges them using the concept of fusion moves. The merged parcellation initialises the next iteration,forcing all modalities to converge towards a set of mutually informed parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
Publisher	Springer Verlag
Pages	148-156
Number of pages	9
ISBN (Print)	9783319467191
DOIs	https://doi.org/10.1007/978-3-319-46720-7_18
State	Published - 2016
Externally published	Yes
Event	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 21 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9900 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	21/10/16 → 21/10/16

Access to Document

10.1007/978-3-319-46720-7_18

Cite this

Parisot, S., Glocker, B., Schirmer, M. D., & Rueckert, D. (2016). GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, W. Wells, & G. Unal (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 148-156). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_18

Parisot, Sarah ; Glocker, Ben ; Schirmer, Markus D. et al. / GraMPa : Graph-based multi-modal parcellation of the cortex using fusion moves. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Sebastian Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal. Springer Verlag, 2016. pp. 148-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f84b9d6116314da1bf9ea71b4b74af46,

title = "GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves",

abstract = "Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven parcellation is a natural approach,but suffers from the lack of reliability of connectivity data. Combining modalities in the parcellation task has the potential to yield more robust parcellations,yet hasn{\textquoteright}t been explored much. In this paper,we propose a graphbased multi-modal parcellation method that iteratively computes a set of modality specific parcellations and merges them using the concept of fusion moves. The merged parcellation initialises the next iteration,forcing all modalities to converge towards a set of mutually informed parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.",

author = "Sarah Parisot and Ben Glocker and Schirmer, {Markus D.} and Daniel Rueckert",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46720-7_18",

language = "English",

isbn = "9783319467191",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "148--156",

editor = "Sebastian Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and William Wells and Gozde Unal",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",

}

Parisot, S, Glocker, B, Schirmer, MD & Rueckert, D 2016, GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves. in S Ourselin, L Joskowicz, MR Sabuncu, W Wells & G Unal (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. 9900 LNCS, Springer Verlag, pp. 148-156, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 21/10/16. https://doi.org/10.1007/978-3-319-46720-7_18

GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves. / Parisot, Sarah; Glocker, Ben; Schirmer, Markus D. et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Sebastian Ourselin; Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal. Springer Verlag, 2016. p. 148-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

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

TY - GEN

T1 - GraMPa

T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

AU - Parisot, Sarah

AU - Glocker, Ben

AU - Schirmer, Markus D.

AU - Rueckert, Daniel

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven parcellation is a natural approach,but suffers from the lack of reliability of connectivity data. Combining modalities in the parcellation task has the potential to yield more robust parcellations,yet hasn’t been explored much. In this paper,we propose a graphbased multi-modal parcellation method that iteratively computes a set of modality specific parcellations and merges them using the concept of fusion moves. The merged parcellation initialises the next iteration,forcing all modalities to converge towards a set of mutually informed parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.

AB - Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven parcellation is a natural approach,but suffers from the lack of reliability of connectivity data. Combining modalities in the parcellation task has the potential to yield more robust parcellations,yet hasn’t been explored much. In this paper,we propose a graphbased multi-modal parcellation method that iteratively computes a set of modality specific parcellations and merges them using the concept of fusion moves. The merged parcellation initialises the next iteration,forcing all modalities to converge towards a set of mutually informed parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.

UR - http://www.scopus.com/inward/record.url?scp=84996486175&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46720-7_18

DO - 10.1007/978-3-319-46720-7_18

M3 - Conference contribution

AN - SCOPUS:84996486175

SN - 9783319467191

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 148

EP - 156

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

A2 - Ourselin, Sebastian

A2 - Joskowicz, Leo

A2 - Sabuncu, Mert R.

A2 - Wells, William

A2 - Unal, Gozde

PB - Springer Verlag

Y2 - 21 October 2016 through 21 October 2016

ER -

Parisot S, Glocker B, Schirmer MD, Rueckert D. GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves. In Ourselin S, Joskowicz L, Sabuncu MR, Wells W, Unal G, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 148-156. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46720-7_18

GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this