Automatic Vein Segmentation and Lesion Detection: From SWI‐MIPs to MR Venograms

Barnes, Samuel; Barth, Markus; Koopmans, P.P.

doi:10.1002/9780470905203.ch32

Cited by 1 publication

(2 citation statements)

References 18 publications

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“…! as previously reported [7,24]. This effect can be partially mitigated with asymmetric penalty terms [28], with different configurations of local neighborhood [9], or, as investigated in this study, with data-dependent interactions.…”

Section: A Mrf/crf Modelingsupporting

confidence: 66%

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Automatic SWI Venography Segmentation Using Conditional Random Fields

Bériault

Xiao

Collins

et al. 2015

IEEE Trans. Med. Imaging

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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.

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Section: A Mrf/crf Modelingsupporting

confidence: 66%

“…Shape-based techniques are particularly robust for enhancing vessels in low-contrast areas and have been used for enhancing SWI venography [22][23][24][25][26]. However, these filters make strong geometric assumptions that can under-segment surface veins, especially in reversed-contrast SWI, and vascular anomalies (e.g.…”

Section: A Previous Workmentioning

confidence: 99%