Summary
Objectives: Cerebral vascular malformations might, caused by ruptures, lead to strokes. The rupture
risk depends to a great extent on the individual anatomy of the vasculature. The 3D
Time-of-Flight (TOF) MRA technique is one of the most commonly used non-invasive imaging
techniques to obtain knowledge about the individual vascular anatomy. Unfortunately
TOF images exhibit drawbacks for segmentation and direct volume visualization of the
vasculature. To overcome these drawbacks an initial segmentation of the brain tissue
is required.
Methods: After preprocessing of the data is applied the low-intensity tissues surrounding
the brain are segmented using region growing. In a following step this segmentation
is used to extract supporting points at the border of the brain for a graph-based
contour extraction. Finally a consistency check is performed to identify local outliers
which are corrected using non-linear registration.
Results: A quantitative validation of the method proposed was performed on 18 clinical datasets
based on manual segmentations. A mean Dice coefficient of 0.989 was achieved while
in average 99.56% of all vessel voxels were included by the brain segmentation. A
comparison to the results yielded by three commonly used tools for brain segmentation
revealed that the method described achieves better results, using TOF images as input,
which are within the inter-observer variability.
Conclusion: The method suggested allows a robust and automatic segmentation of brain tissue in
TOF images. It is especially helpful to improve the automatic segmentation or direct
volume rendering of the cerebral vascular system.
Keywords
Computer-assisted image processing - brain vascular disorders - brain - image segmentation
- magnetic resonance angiography
