Unsupervised Fiber Bundles Registration using Weighted Measures Geometric Demons

Abstract : Brain image registration aims at reducing anatomical variability across subjects to create a common space for group analysis. Multi-modal approaches intend to minimize cortex shape variations along with internal structures, such as fiber bundles. A di ficulty is that it requires a prior identi fication of these structures, which remains a challenging task in the absence of a complete reference atlas. We propose an extension of the log-Geometric Demons for jointly registering images and fi ber bundles without the need of point or ber correspondences. By representing fi ber bundles as Weighted Measures we can register subjects with di fferent numbers of fiber bundles. The ef ficacy of our algorithm is demonstrated by registering simultaneously T1 images and between 37 and 88 ber bundles depending on each of the ten subject used. We compare results with a multi-modal T1 + Fractional Anisotropy (FA) and a tensor-based registration algorithms and obtain superior performance with our approach.
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Viviana Siless, Sergio Medina, Pierre Fillard, Bertrand Thirion. Unsupervised Fiber Bundles Registration using Weighted Measures Geometric Demons. Workshop on Multi Modal Brain Image Analysis, Li Shen - Tianming Liu - Pew-Thian Yap - Heng Huang - Dinggang Shen -Carl-Fredrik Westin, Sep 2013, Nagoya, Japan. ⟨hal-00853582⟩

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