Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses

Hao Xu; Bertrand Thirion; Stéphanie Allassonnière

doi:10.1007/978-3-642-40760-4_74

Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses

Hao Xu
, Bertrand Thirion
, Stéphanie Allassonnière

École Polytechnique

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

Abstract

Traditional analyses of Functional Magnetic Resonance Imaging (fMRI) use little anatomical information. The registration of the images to a template is based on the individual anatomy and ignores functional information; subsequently detected activations are not confined to gray matter (GM). In this paper, we propose a statistical model to estimate a probabilistic atlas from functional and T1 MRIs that summarizes both anatomical and functional information and the geometric variability of the population. Registration and Segmentation are performed jointly along the atlas estimation and the functional activity is constrained to the GM, increasing the accuracy of the atlas.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages	592-599
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-40760-4_74
Publication status	Published - 24 Oct 2013
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sept 2013 → 26 Sept 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	8151 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	22/09/13 → 26/09/13

Keywords

Probabilistic atlas
T1 MRI and fMRI
atlas-based segmentation
geometric variability
joint registration segmentation
multi-modal

Access to Document

10.1007/978-3-642-40760-4_74

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Xu, H., Thirion, B., & Allassonnière, S. (2013). Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 3 ed., pp. 592-599). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_74

Xu, Hao ; Thirion, Bertrand ; Allassonnière, Stéphanie. / Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3. ed. 2013. pp. 592-599 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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abstract = "Traditional analyses of Functional Magnetic Resonance Imaging (fMRI) use little anatomical information. The registration of the images to a template is based on the individual anatomy and ignores functional information; subsequently detected activations are not confined to gray matter (GM). In this paper, we propose a statistical model to estimate a probabilistic atlas from functional and T1 MRIs that summarizes both anatomical and functional information and the geometric variability of the population. Registration and Segmentation are performed jointly along the atlas estimation and the functional activity is constrained to the GM, increasing the accuracy of the atlas.",

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Xu, H, Thirion, B & Allassonnière, S 2013, Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8151 LNCS, pp. 592-599, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 22/09/13. https://doi.org/10.1007/978-3-642-40760-4_74

Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses. / Xu, Hao; Thirion, Bertrand; Allassonnière, Stéphanie.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3. ed. 2013. p. 592-599 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3).

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

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AU - Thirion, Bertrand

AU - Allassonnière, Stéphanie

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N2 - Traditional analyses of Functional Magnetic Resonance Imaging (fMRI) use little anatomical information. The registration of the images to a template is based on the individual anatomy and ignores functional information; subsequently detected activations are not confined to gray matter (GM). In this paper, we propose a statistical model to estimate a probabilistic atlas from functional and T1 MRIs that summarizes both anatomical and functional information and the geometric variability of the population. Registration and Segmentation are performed jointly along the atlas estimation and the functional activity is constrained to the GM, increasing the accuracy of the atlas.

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KW - Probabilistic atlas

KW - T1 MRI and fMRI

KW - atlas-based segmentation

KW - geometric variability

KW - joint registration segmentation

KW - multi-modal

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Y2 - 22 September 2013 through 26 September 2013

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Xu H, Thirion B, Allassonnière S. Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 3 ed. 2013. p. 592-599. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-40760-4_74

Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses

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