Takemura Akihiro, Suzuki Masayuki, Harauchi Hajime, Okumura Yusuke
Division of Health Sciences, Graduate School of Medical Science, Kanazawa University, 5-11-80, Kodatsuno, Kanazawa 920-0942, Japan.
Igaku Butsuri. 2006;26(4):187-98.
To improve the accuracy and robustness of 2D/3D registration of digital subtraction angiography images and magnetic resonance angiography (MRA) data, we have developed an automatic method for anatomical labeling of the cerebral arteries in MRA data. The anatomical labeling method is a location-based method which segments an artery tree to branches and classifies the branches into labeled segments, i.e., internal carotid arteries (ICA), basilar artery (BA), middle cerebral arteries (MCA), A1 segments of the anterior cerebral artery (ACA(A1)), other segments of the anterior cerebral artery (ACA), posterior communication arteries (PcomA) and posterior cerebral arteries (PCA), according to their location. Arteries were extracted from MRA data for this labeling method by the region-growing technique. Fifteen cases were examined to evaluate the method accuracy. The number of correctly segmented voxels in each artery segment was determined, and the correct labeling percentage was calculated based on the total number of voxels of the artery. Mean percentages were as follows: ACA, 82.7%; Right (R-) ACA(A1), 47.1%; Left (L-) ACA(A1), 46.1%; R-MCA, 80.4%; L-MCA, 74.1%; R-PcomA, 0.0%; L-PcomA, 3.3%; R-PCA, 60.3%; LPCA, 66.9%; R-ICA, 90.7%; L-ICA, 90.7%; BA, 89.9%; and total arteries, 84.1%. The ACA, MCA, ICA and BA were consistently identified correctly.
