Region segmentation for sparse decompositions: better brain parcellations from rest fMRI

Abstract : Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas. Independent components analysis is the reference approach to estimate spatial components from weakly structured data such as brain signal time courses; each of these components may be referred to as a brain network and the whole set of components can be conceptualized as a brain functional atlas. Recently, new methods using a sparsity prior have emerged to deal with low signal-to-noise ratio data. However, even when using sophisticated priors, the results may not be very sparse and most often do not separate the spatial components into brain regions. This work presents post-processing techniques that automatically sparsify brain maps and separate regions properly using geometric operations, and compares these techniques according to faithfulness to data and stability metrics. In particular, among threshold-based approaches, hysteresis thresholding and random walker segmentation, the latter improves significantly the stability of both dense and sparse models.
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Sparsity Techniques in Medical Imaging, Sep 2014, Boston, United States. pp.8
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https://hal.inria.fr/hal-01093944
Contributeur : Alexandre Abraham <>
Soumis le : jeudi 11 décembre 2014 - 13:58:50
Dernière modification le : lundi 4 juin 2018 - 15:42:02
Document(s) archivé(s) le : jeudi 12 mars 2015 - 10:45:53

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  • HAL Id : hal-01093944, version 1
  • ARXIV : 1412.3925

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Alexandre Abraham, Elvis Dohmatob, Bertrand Thirion, Dimitris Samaras, Gael Varoquaux. Region segmentation for sparse decompositions: better brain parcellations from rest fMRI. Sparsity Techniques in Medical Imaging, Sep 2014, Boston, United States. pp.8. 〈hal-01093944〉

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