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Methods Inf Med 2012; 51(05): 415-422
DOI: 10.3414/ME11-02-0037
Focus Theme – Original Articles
Schattauer GmbH

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

N. D. Forkert
1   Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
,
A. Schmidt-Richberg
2   Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
,
J. Fiehler
3   Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
,
T. Illies
3   Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
,
D. Möller
4   Department Computer Engineering, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Hamburg, Germany
,
H. Handels
2   Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
,
D. Säring
1   Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
› Author Affiliations
Further Information

Publication History

received:15 October 2011

accepted:30 January 2012

Publication Date:
20 January 2018 (online)

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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
 

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