Abstract
Patient-specific cerebrovascular modeling provides essential information to facilitate the identification of vessel-free trajectories in functional neurosurgery. However, standard gadolinium models used clinically are often incomplete due to the extent of manual labor required to segment the vessels and because gadolinium contrast decreases rapidly with vessel size. In this work, we propose an automatic method, based on the Markov Random Field (MRF) theory, to segment venous blood vessels from dense susceptibility-weighted imaging (SWI) venography datasets. Unlike conventional isotropic auto-logistic MRF, our MRF design anisotropically favors the neighboring influence of voxels classified as “vessels” to better preserve thin vessels imaged by SWI. Results show that MRF segmentation of deep veins compares well with standard scale-space vesselness analysis. Most importantly, we demonstrate automatic segmentation of superficial veins on SWI and creation of denser 3D vascular models that may improve clinical gadolinium-based models.
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Bériault, S., Archambault-Wallenburg, M., Sadikot, A.F., Louis Collins, D., Bruce Pike, G. (2014). Automatic Markov Random Field Segmentation of Susceptibility-Weighted MR Venography. In: Erdt, M., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2013. Lecture Notes in Computer Science(), vol 8361. Springer, Cham. https://doi.org/10.1007/978-3-319-05666-1_6
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DOI: https://doi.org/10.1007/978-3-319-05666-1_6
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