title: Automatic contrast phase estimation in CT volumes.
abstract: We propose an automatic algorithm for phase labeling that relies on the intensity changes in anatomical regions due to the contrast agent propagation. The regions (specified by aorta, vena cava, liver, and kidneys) are first detected by a robust learning-based discriminative algorithm. The intensities inside each region are then used in multi-class LogitBoost classifiers to independently estimate the contrast phase. Each classifier forms a node in a decision tree which is used to obtain the final phase label. Combining independent classification from multiple regions in a tree has the advantage when one of the region detectors fail or when the phase training example database is imbalanced. We show on a dataset of 1016 volumes that the system correctly classifies native phase in 96.2% of the cases, hepatic dominant phase (92.2%), hepatic venous phase (96.7%), and equilibrium phase (86.4%) in 7 seconds on average.
authors: Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA.
authors: Michal
authors: M
authors: Sofka
authors: Dijia
authors: D
authors: Wu
authors: Michael
authors: M
authors: Sühling
authors: David
authors: D
authors: Liu
authors: Christian
authors: C
authors: Tietjen
authors: Grzegorz
authors: G
authors: Soza
authors: S Kevin
authors: SK
authors: Zhou
mesh_terms: D000465:Algorithms; D001011:Aorta / Q000473:pathology; D001331:Automation; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D003287:Contrast Media / Q000494:pharmacology*; D003663:Decision Trees; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000473:pathology; D008099:Liver / Q000473:pathology; D015233:Models, Statistical; D009206:Myocardium / Q000473:pathology; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D003287:Contrast Media