This paper presents an algorithm for the automatic detection of intravenous contrast in CT scans. This is useful e.g. for quality control, given the unreliability of the existing DICOM contrast metadata. The algorithm is based on a hybrid discriminative-generative probabilistic model. A discriminative detector localizes enhancing regions of interest in the scan. Then a generative classi\ufb01er optimally fuses evidence gathered from those regions into an e\ufb03cient, probabilistic prediction. The main contribution is in the generative part. It assigns optimal weights to the detected organs based on their learned degree of enhancement under contrast material. The model is robust with respect to missing organs, patients geometry, pathology and settings. Validation is performed on a database of 400 highly variable patients CT scans. Results indicate detection accuracy greater than 91% at \u223c 1 second per scan.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents an algorithm for the automatic detection of intravenous contrast in CT scans. This is useful e.g. for quality control, given the unreliability of the existing DICOM contrast metadata. The algorithm is based on a hybrid discriminative-generative probabilistic model. A discriminative detector localizes enhancing regions of interest in the scan. 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