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Automated artery and vein detection in 4D-CT data with an unsupervised classification algorithm of the time intensity curves

: Laue, H.O.A.; Oei, M.T.H.; Chen, L.; Kompan, I.N.; Hahn, H.K.; Prokop, M.; Manniesing, R.


Ourselin, S. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical Imaging 2013. Image Processing. Vol.1 : 10-12 February 2013, Lake Buena Vista, Florida, United States
Bellingham, WA: SPIE, 2013 (Proceedings of SPIE 8669)
ISBN: 978-0-8194-9443-6
ISSN: 1605-7422
Paper 86691W
Conference "Medical Imaging - Image Processing" <2013, Lake Buena Vista/Fla.>
Fraunhofer MEVIS ()

In this work a fully automated detection method for artery input function (AIF) and venous output function (VOF) in 4D-computer tomography (4D-CT) data is presented based on unsupervised classification of the time intensity curves (TIC) as input data. Bone and air voxels are first masked out using thresholding of the baseline measurement. The TICs for each remaining voxel are converted to time-concentration-curves (TCC) by subtracting the baseline value from the TIC. Then, an unsupervised K-means classifier is applied to each TCC with an area under the curve (AUC) larger than 95% of the maximum AUC of all TCCs. The results are three clusters, which yield average TCCs for vein and artery voxels in the brain, respectively. A third cluster generally represents a vessel outside the brain. The algorithm was applied to five 4D-CT patient data who were scanned on the suspicion of ischemic stroke. For all ve patients, the algorithm yields reasonable classification of arteries and veins as well as reasonable and reproducible AIFs and VOF. To our knowledge, this is the first application of an unsupervised classification method to automatically identify arteries and veins in 4D-CT data. Preliminary results show the feasibility of using K-means clustering for the purpose of artery-vein detection in 4D-CT patient data.