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Automatic SWI Venography Segmentation Using Conditional Random Fields.
- Source :
-
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2015 Dec; Vol. 34 (12), pp. 2478-91. Date of Electronic Publication: 2015 Jun 05. - Publication Year :
- 2015
-
Abstract
- Susceptibility-weighted imaging (SWI) venography can produce detailed venous contrast and complement arterial dominated MR angiography (MRA) techniques. However, these dense reversed-contrast SWI venograms pose new segmentation challenges. We present an automatic method for whole-brain venous blood segmentation in SWI using Conditional Random Fields (CRF). The CRF model combines different first and second order potentials. First-order association potentials are modeled as the composite of an appearance potential, a Hessian-based shape potential and a non-linear location potential. Second-order interaction potentials are modeled using an auto-logistic (smoothing) potential and a data-dependent (edge) potential. Minimal post-processing is used for excluding voxels outside the brain parenchyma and visualizing the surface vessels. The CRF model is trained and validated using 30 SWI venograms acquired within a population of deep brain stimulation (DBS) patients (age range [Formula: see text] years). Results demonstrate robust and consistent segmentation in deep and sub-cortical regions (median kappa = 0.84 and 0.82), as well as in challenging mid-sagittal and surface regions (median kappa = 0.81 and 0.83) regions. Overall, this CRF model produces high-quality segmentation of SWI venous vasculature that finds applications in DBS for minimizing hemorrhagic risks and other surgical and non-surgical applications.
Details
- Language :
- English
- ISSN :
- 1558-254X
- Volume :
- 34
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- IEEE transactions on medical imaging
- Publication Type :
- Academic Journal
- Accession number :
- 26057611
- Full Text :
- https://doi.org/10.1109/TMI.2015.2442236