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Template-Based Multimodal Joint Generative Model of Brain Data

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Template-Based Multimodal Joint Generative Model of Brain Data. / Cardoso, M. Jorge; Sudre, Carole H.; Modat, Marc; Ourselin, Sebastien.

In: Information processing in medical imaging : proceedings of the ... conference, Vol. 24, 01.01.2015, p. 17-29.

Research output: Contribution to journalArticle

Harvard

Cardoso, MJ, Sudre, CH, Modat, M & Ourselin, S 2015, 'Template-Based Multimodal Joint Generative Model of Brain Data', Information processing in medical imaging : proceedings of the ... conference, vol. 24, pp. 17-29.

APA

Cardoso, M. J., Sudre, C. H., Modat, M., & Ourselin, S. (2015). Template-Based Multimodal Joint Generative Model of Brain Data. Information processing in medical imaging : proceedings of the ... conference, 24, 17-29.

Vancouver

Cardoso MJ, Sudre CH, Modat M, Ourselin S. Template-Based Multimodal Joint Generative Model of Brain Data. Information processing in medical imaging : proceedings of the ... conference. 2015 Jan 1;24:17-29.

Author

Cardoso, M. Jorge ; Sudre, Carole H. ; Modat, Marc ; Ourselin, Sebastien. / Template-Based Multimodal Joint Generative Model of Brain Data. In: Information processing in medical imaging : proceedings of the ... conference. 2015 ; Vol. 24. pp. 17-29.

Bibtex Download

@article{d4299d546e8f40ef8722c5fd76662644,
title = "Template-Based Multimodal Joint Generative Model of Brain Data",
abstract = "The advent of large of multi-modal imaging databases opens up the opportunity to learn how local intensity patterns covariate between multiple modalities. These models can then be used to describe expected intensities in an unseen image modalities given one or multiple observations, or to detect deviations (e.g. pathology) from the expected intensity patterns. In this work, we propose a template-based multi-modal generative mixture-model of imaging data and apply it to the problems of inlier/outlier pattern classification and image synthesis. Results on synthetic and patient data demonstrate that the proposed method is able to synthesise unseen data and accurately localise pathological regions, even in the presence of large abnormalities. It also demonstrates that the proposed model can provide accurate and uncertainty-aware intensity estimates of expected imaging patterns.",
author = "Cardoso, {M. Jorge} and Sudre, {Carole H.} and Marc Modat and Sebastien Ourselin",
year = "2015",
month = jan,
day = "1",
language = "English",
volume = "24",
pages = "17--29",
journal = "Information Processing in Medical Imaging",
issn = "1011-2499",
publisher = "Springer Verlag",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Template-Based Multimodal Joint Generative Model of Brain Data

AU - Cardoso, M. Jorge

AU - Sudre, Carole H.

AU - Modat, Marc

AU - Ourselin, Sebastien

PY - 2015/1/1

Y1 - 2015/1/1

N2 - The advent of large of multi-modal imaging databases opens up the opportunity to learn how local intensity patterns covariate between multiple modalities. These models can then be used to describe expected intensities in an unseen image modalities given one or multiple observations, or to detect deviations (e.g. pathology) from the expected intensity patterns. In this work, we propose a template-based multi-modal generative mixture-model of imaging data and apply it to the problems of inlier/outlier pattern classification and image synthesis. Results on synthetic and patient data demonstrate that the proposed method is able to synthesise unseen data and accurately localise pathological regions, even in the presence of large abnormalities. It also demonstrates that the proposed model can provide accurate and uncertainty-aware intensity estimates of expected imaging patterns.

AB - The advent of large of multi-modal imaging databases opens up the opportunity to learn how local intensity patterns covariate between multiple modalities. These models can then be used to describe expected intensities in an unseen image modalities given one or multiple observations, or to detect deviations (e.g. pathology) from the expected intensity patterns. In this work, we propose a template-based multi-modal generative mixture-model of imaging data and apply it to the problems of inlier/outlier pattern classification and image synthesis. Results on synthetic and patient data demonstrate that the proposed method is able to synthesise unseen data and accurately localise pathological regions, even in the presence of large abnormalities. It also demonstrates that the proposed model can provide accurate and uncertainty-aware intensity estimates of expected imaging patterns.

UR - http://www.scopus.com/inward/record.url?scp=84942569101&partnerID=8YFLogxK

M3 - Article

C2 - 26221664

AN - SCOPUS:84942569101

VL - 24

SP - 17

EP - 29

JO - Information Processing in Medical Imaging

JF - Information Processing in Medical Imaging

SN - 1011-2499

ER -

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