PROJECT TITLE :

Automatic SWI Venography Segmentation Using Conditional Random Fields

ABSTRACT:

Susceptibility-weighted imaging (SWI) venography will manufacture detailed venous contrast and complement arterial dominated MR angiography (MRA) techniques. But, these dense reversed-contrast SWI venograms create new segmentation challenges. We gift an automatic methodology 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-primarily based shape potential and a non-linear location potential. Second-order interaction potentials are modeled using an auto-logistic (smoothing) potential and a information-dependent (edge) potential. Minimal post-processing is employed for excluding voxels outside the brain parenchyma and visualizing the surface vessels. The CRF model is trained and validated using thirty SWI venograms acquired within a population of deep brain stimulation (DBS) patients (age vary $= 43mathchar"702D73$ years). Results demonstrate robust and consistent segmentation in deep and sub-cortical regions (median $rm kappa= zero.84$ and zero.82), and in difficult mid-sagittal and surface regions (median $rm kappa= zero.81$ 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.