Summary
Objectives: Exact cerebrovascular segmentations are required for several applications in today’s
clinical routine. A major drawback of typical automatic segmentation methods is the
occurrence of gaps within the segmentation. These gaps are typically located at small
vessel structures exhibiting low intensities. Manual correction is very time-consuming
and not suitable in clinical practice. This work presents a post-processing method
for the automatic detection and closing of gaps in cerebrovascular segmentations.
Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation,
which enables the detection of corresponding vessel endpoints. These endpoints are
then used to detect possible connections to other 3D centerline voxels with a graph-based
approach. After consistency check, reasonable detected paths are expanded to the vessel
boundaries using a level set approach and combined with the initial segmentation.
Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels
and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight
magnetic resonance angiography datasets. The results show that the presented method
is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation
gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy
compared to the initial segmentations. A further evaluation based on 10 real automatic
segmentations from the same datasets shows that the proposed method detects 35 additional
connections in average per dataset, whereas 92.7% were rated as correct by a medical
expert.
Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations
and of following analysis outcomes.
Keywords
Magnetic resonance imaging - computer-assisted image analysis - cerebrovascular system
- segmentation gaps - correction
