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High Angular Resolution Diffusion MRI Segmentation Using Region-Based Statistical Surface Evolution

Maxime Descoteaux 1 Rachid Deriche 2
2 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : In this article we develop a new method to segment high angular resolution diffusion imaging (HARDI) data. We first estimate the orientation distribution function (ODF) using a fast and robust spherical harmonic (SH) method. Then, we use a region-based statistical surface evolution on this image of ODFs to efficiently find coherent white matter fiber bundles. We show that our method is appropriate to propagate through regions of fiber crossings and we show that our results outperform state-of-the-art diffusion tensor (DT) imaging segmentation methods, inherently limited by the DT model. Results obtained on synthetic data, on a biological phantom, on real datasets and on all 13 subjects of a public NMR database show that our method is reproducible, automatic and brings a strong added value to diffusion MRI segmentation.
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https://hal.inria.fr/inria-00423393
Contributor : Alain Monteil <>
Submitted on : Friday, October 9, 2009 - 5:01:14 PM
Last modification on : Wednesday, May 5, 2021 - 3:06:21 AM

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Maxime Descoteaux, Rachid Deriche. High Angular Resolution Diffusion MRI Segmentation Using Region-Based Statistical Surface Evolution. Journal of Mathematical Imaging and Vision, Springer Verlag, 2008, ⟨10.1007/s10851-008-0071-8⟩. ⟨inria-00423393⟩

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