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Improved Segmentation of White Matter Tracts with Adaptive Riemannian Metrics

1School of Computing, University of Utah, Salt Lake City, UT, USA. Electronic address:
2Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
Elsevier Science
Publication Date:
Med Image Anal
Volume Number:
Issue Number:
Med Image Anal. 2014 Jan;18(1):161-75.
PubMed ID:
Conformal factor, Diffusion tensor imaging, Front-propagation, Geodesic, Riemannian manifold
Appears in Collections:
R01 MH080826/MH/NIMH NIH HHS/United States
R01 MH084795/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Hao X., Zygmunt K., Whitaker R.T., Fletcher P.T. Improved Segmentation of White Matter Tracts with Adaptive Riemannian Metrics. Med Image Anal. 2014 Jan;18(1):161-75. PMID: 24211814. PMCID: PMC3898892.
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We present a novel geodesic approach to segmentation of white matter tracts from diffusion tensor imaging (DTI). Compared to deterministic and stochastic tractography, geodesic approaches treat the geometry of the brain white matter as a manifold, often using the inverse tensor field as a Riemannian metric. The white matter pathways are then inferred from the resulting geodesics, which have the desirable property that they tend to follow the main eigenvectors of the tensors, yet still have the flexibility to deviate from these directions when it results in lower costs. While this makes such methods more robust to noise, the choice of Riemannian metric in these methods is ad hoc. A serious drawback of current geodesic methods is that geodesics tend to deviate from the major eigenvectors in high-curvature areas in order to achieve the shortest path. In this paper we propose a method for learning an adaptive Riemannian metric from the DTI data, where the resulting geodesics more closely follow the principal eigenvector of the diffusion tensors even in high-curvature regions. We also develop a way to automatically segment the white matter tracts based on the computed geodesics. We show the robustness of our method on simulated data with different noise levels. We also compare our method with tractography methods and geodesic approaches using other Riemannian metrics and demonstrate that the proposed method results in improved geodesics and segmentations using both synthetic and real DTI data.

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