Geodesic information flows

M. Jorge Cardoso; Robin Wolz; Marc Modat; Nick C. Fox; Daniel Rueckert; Sebastien Ourselin

doi:10.1007/978-3-642-33418-4_33

Geodesic information flows

M. Jorge Cardoso, Robin Wolz, Marc Modat, Nick C. Fox, Daniel Rueckert, Sebastien Ourselin

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

35 Scopus citations

Abstract

Homogenising the availability of manually generated information in large databases has been a key challenge of medical imaging for many years. Due to the time consuming nature of manually segmenting, parcellating and localising landmarks in medical images, these sources of information tend to be scarce and limited to small, and sometimes morphologically similar, subsets of data. In this work we explore a new framework where these sources of information can be propagated to morphologically dissimilar images by diffusing and mapping the information through intermediate steps. The spatially variant data embedding uses the local morphology and intensity similarity between images to diffuse the information only between locally similar images. This framework can thus be used to propagate any information from any group of subject to every other subject in a database with great accuracy. Comparison to state-of-the-art propagation methods showed highly statistically significant (p < 10−4) improvements in accuracy when propagating both structural parcelations and brain segmentations geodesically.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
Editors	Georg Langs, Antonio Criminisi, Bjoern H. Menze, Bjoern H. Menze, Zhuowen Tu, Georg Langs, Nicholas Ayache, Hervé Delingette, Albert Montillo, Le Lu, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	262-270
Number of pages	9
ISBN (Print)	9783642334177
DOIs	https://doi.org/10.1007/978-3-642-33418-4_33
State	Published - 2012
Externally published	Yes
Event	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France Duration: 5 Oct 2012 → 5 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7511 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	5/10/12 → 5/10/12

Access to Document

10.1007/978-3-642-33418-4_33

Cite this

Cardoso, M. J., Wolz, R., Modat, M., Fox, N. C., Rueckert, D., & Ourselin, S. (2012). Geodesic information flows. In G. Langs, A. Criminisi, B. H. Menze, B. H. Menze, Z. Tu, G. Langs, N. Ayache, H. Delingette, A. Montillo, L. Lu, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings (pp. 262-270). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33418-4_33

Cardoso, M. Jorge ; Wolz, Robin ; Modat, Marc et al. / Geodesic information flows. Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. editor / Georg Langs ; Antonio Criminisi ; Bjoern H. Menze ; Bjoern H. Menze ; Zhuowen Tu ; Georg Langs ; Nicholas Ayache ; Hervé Delingette ; Albert Montillo ; Le Lu ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 262-270 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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author = "Cardoso, {M. Jorge} and Robin Wolz and Marc Modat and Fox, {Nick C.} and Daniel Rueckert and Sebastien Ourselin",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 ; Conference date: 05-10-2012 Through 05-10-2012",

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doi = "10.1007/978-3-642-33418-4_33",

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editor = "Georg Langs and Antonio Criminisi and Menze, {Bjoern H.} and Menze, {Bjoern H.} and Zhuowen Tu and Georg Langs and Nicholas Ayache and Herv{\'e} Delingette and Albert Montillo and Le Lu and Polina Golland and Kensaku Mori",

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Cardoso, MJ, Wolz, R, Modat, M, Fox, NC, Rueckert, D & Ourselin, S 2012, Geodesic information flows. in G Langs, A Criminisi, BH Menze, BH Menze, Z Tu, G Langs, N Ayache, H Delingette, A Montillo, L Lu, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7511 LNCS, Springer Verlag, pp. 262-270, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 5/10/12. https://doi.org/10.1007/978-3-642-33418-4_33

Geodesic information flows. / Cardoso, M. Jorge; Wolz, Robin; Modat, Marc et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. ed. / Georg Langs; Antonio Criminisi; Bjoern H. Menze; Bjoern H. Menze; Zhuowen Tu; Georg Langs; Nicholas Ayache; Hervé Delingette; Albert Montillo; Le Lu; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 262-270 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS).

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

TY - GEN

T1 - Geodesic information flows

AU - Cardoso, M. Jorge

AU - Wolz, Robin

AU - Modat, Marc

AU - Fox, Nick C.

AU - Rueckert, Daniel

AU - Ourselin, Sebastien

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2012.

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N2 - Homogenising the availability of manually generated information in large databases has been a key challenge of medical imaging for many years. Due to the time consuming nature of manually segmenting, parcellating and localising landmarks in medical images, these sources of information tend to be scarce and limited to small, and sometimes morphologically similar, subsets of data. In this work we explore a new framework where these sources of information can be propagated to morphologically dissimilar images by diffusing and mapping the information through intermediate steps. The spatially variant data embedding uses the local morphology and intensity similarity between images to diffuse the information only between locally similar images. This framework can thus be used to propagate any information from any group of subject to every other subject in a database with great accuracy. Comparison to state-of-the-art propagation methods showed highly statistically significant (p < 10−4) improvements in accuracy when propagating both structural parcelations and brain segmentations geodesically.

AB - Homogenising the availability of manually generated information in large databases has been a key challenge of medical imaging for many years. Due to the time consuming nature of manually segmenting, parcellating and localising landmarks in medical images, these sources of information tend to be scarce and limited to small, and sometimes morphologically similar, subsets of data. In this work we explore a new framework where these sources of information can be propagated to morphologically dissimilar images by diffusing and mapping the information through intermediate steps. The spatially variant data embedding uses the local morphology and intensity similarity between images to diffuse the information only between locally similar images. This framework can thus be used to propagate any information from any group of subject to every other subject in a database with great accuracy. Comparison to state-of-the-art propagation methods showed highly statistically significant (p < 10−4) improvements in accuracy when propagating both structural parcelations and brain segmentations geodesically.

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BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings

A2 - Langs, Georg

A2 - Criminisi, Antonio

A2 - Menze, Bjoern H.

A2 - Tu, Zhuowen

A2 - Langs, Georg

A2 - Ayache, Nicholas

A2 - Delingette, Hervé

A2 - Montillo, Albert

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Cardoso MJ, Wolz R, Modat M, Fox NC, Rueckert D, Ourselin S. Geodesic information flows. In Langs G, Criminisi A, Menze BH, Menze BH, Tu Z, Langs G, Ayache N, Delingette H, Montillo A, Lu L, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 262-270. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33418-4_33

Geodesic information flows

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