title: Gap-free segmentation of vascular networks with automatic image processing pipeline.
abstract: Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes.
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan St, 218 SEO, M/C 063, Chicago, IL 60607-7000, USA.
authors.forename: Chih-Yang
authors.initials: CY
authors.lastname: Hsu
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan St, 218 SEO, M/C 063, Chicago, IL 60607-7000, USA.
authors.forename: Mahsa
authors.initials: M
authors.lastname: Ghaffari
authors.affiliation: Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Ali
authors.initials: A
authors.lastname: Alaraj
authors.affiliation: Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Flannery
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan St, 218 SEO, M/C 063, Chicago, IL 60607-7000, USA; Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA; Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA; Center for MR Research, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Xiaohong Joe
authors.initials: XJ
authors.lastname: Zhou
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan St, 218 SEO, M/C 063, Chicago, IL 60607-7000, USA; Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA. Electronic address: linninge@uic.edu.
authors.forename: Andreas
authors.initials: A
authors.lastname: Linninger
mesh_terms: D000465:Algorithms; D002533:Cerebral Angiography / Q000379:methods*; D002536:Cerebral Arteries / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Image processing; Modeling; Vessels