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Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI

Abstract : This work provides a framework for modeling and extracting the Cingulum Bundle (CB) from Diffusion-Weighted Imagery (DW-MRI) of the brain. The CB is a tube-like structure in the brain that is of potentially of tremendous importance to clinicians since it may be helpful in diagnosing Schizophrenia. This structure consists of a collection of fibers in the brain that have locally similar diffusion patterns, but vary globally. Standard region-based segmentation techniques adapted to DW-MRI are not suitable here because the diffusion pattern of the CB cannot be described by a global set of simple statistics. Active surface models extended to DW-MRI are not suitable since they allow for arbitrary deformations that give rise to unlikely shapes, which do not respect the tubular geometry of the CB. In this work, we explicitly model the CB as a tube-like surface and construct a general class of energies defined on tube-like surfaces. An example energy of our framework is optimized by a tube that encloses a region that has locally similar diffusion patterns, which differ from the diffusion patterns immediately outside. Modeling the CB as a tube-like surface is a natural shape prior. Since a tube is characterized by a center-line and a radius function, the method is reduced to a 4D (center-line plus radius) curve evolution that is computationally much less costly than an arbitrary surface evolution. The method also provides the center-line of CB, which is potentially of clinical significance.
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Contributor : Alain Monteil <>
Submitted on : Sunday, October 16, 2011 - 11:14:03 PM
Last modification on : Wednesday, August 21, 2019 - 10:22:07 AM
Long-term archiving on: : Thursday, November 15, 2012 - 9:46:51 AM


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  • HAL Id : inria-00632883, version 1



Vandana Mohan, Ganesh Sundaramoorthi, John Melonakos, Marc Niethammer, Marek Kubicki, et al.. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. 2nd MICCAI Workshop on Mathematical Foundations of Computational Anatomy, Oct 2008, New-York, United States. pp.150-159. ⟨inria-00632883⟩