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.

Development of a quantitative intracranial vascular features extraction tool on 3DMRA using semiautomated open‐curve active contour vessel tracing

“…If the length increases slower than 5 percent after an iteration, the trace is then stretched based on the raw image instead as a second stage until the length increase is below 5 percent after an iteration again, or a maximum iteration number is reached. The two stage process also overcomes a well-known disadvantage of the Frangi filter that vessels near an arterial bifurcation are weakened after filtering (35) as the locally tubular structure does not apply to the area of branching. The first stage of tracing ensures the accuracy in parallel vessels, and the second stage avoids the vessel being cut off near bifurcation points.…”

Quantification of near‐wall hemodynamic risk factors in large‐scale cerebral arterial trees

“…Time‐of‐flight (TOF) and phase contrast magnetic resonance angiography (PC‐MRA) were used to measure blood flow, and source imaging data were input for anatomical reconstructions of 6 healthy volunteers and 2 pathological cases, one with intracranial aneurysms and the other with a stenosis. A vesselness enhancement filter was applied to enhance the contrast of cerebral angioarchitecture down to pial arteries (400 μm). After image filtering, information on the vessel centerline and radius of the vascular network were extracted.…”

“…Finally, raw TOF and MRA images were passed to a vessel enhancement filter to successfully identify and segment small pial arteries. We used our in‐house developed vesselness filter to enhance the contrast of the arterial tree in MRA images . The scan parameters of TOF and PC‐MRA are documented in Appendix 1, Table .…”

“…We used our in-house developed vesselness filter to enhance the contrast of the arterial tree in MRA images. 40,41,44 The scan parameters of TOF and PC-MRA are documented in Appendix 1, Table A1. MRA and digital subtraction angiography were acquired under Institute Review Board approval at the University of Illinois at Chicago.…”

Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation

“…Imaging datasets were used for arterial tree reconstructions of six healthy volunteers and two endovascular patient cases. A novel vesselness filter 28,29 was used to enhance the contrast of the cerebral angioarchitecture 30 down to pial arteries (400 µm). Vessel centerline and radius information of the vascular network were extracted.…”