20 March 2015 Probabilistic atlas based labeling of the cerebral vessel tree
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Abstract
Preoperative imaging of the cerebral vessel tree is essential for planning therapy on intracranial stenoses and aneurysms. Usually, a magnetic resonance angiography (MRA) or computed tomography angiography (CTA) is acquired from which the cerebral vessel tree is segmented. Accurate analysis is helped by the labeling of the cerebral vessels, but labeling is non-trivial due to anatomical topological variability and missing branches due to acquisition issues. In recent literature, labeling the cerebral vasculature around the Circle of Willis has mainly been approached as a graph-based problem. The most successful method, however, requires the definition of all possible permutations of missing vessels, which limits application to subsets of the tree and ignores spatial information about the vessel locations.

This research aims to perform labeling using probabilistic atlases that model spatial vessel and label likelihoods. A cerebral vessel tree is aligned to a probabilistic atlas and subsequently each vessel is labeled by computing the maximum label likelihood per segment from label-specific atlases.

The proposed method was validated on 25 segmented cerebral vessel trees. Labeling accuracies were close to 100% for large vessels, but dropped to 50-60% for small vessels that were only present in less than 50% of the set.

With this work we showed that using solely spatial information of the vessel labels, vessel segments from stable vessels (>50% presence) were reliably classified. This spatial information will form the basis for a future labeling strategy with a very loose topological model.
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Martijn Van de Giessen, Martijn Van de Giessen, Jasper P. Janssen, Jasper P. Janssen, Patrick A. Brouwer, Patrick A. Brouwer, Johan H. C. Reiber, Johan H. C. Reiber, Boudewijn P. F. Lelieveldt, Boudewijn P. F. Lelieveldt, Jouke Dijkstra, Jouke Dijkstra, } "Probabilistic atlas based labeling of the cerebral vessel tree", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130V (20 March 2015); doi: 10.1117/12.2081604; https://doi.org/10.1117/12.2081604
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