A Discriminative-Generative Model for Detecting Intravenous Contrast in CT Images

  • Antonio Criminisi
  • Krishna Juluru
  • Sayan Pathak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)


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 classifier optimally fuses evidence gathered from those regions into an efficient, 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 ~1 second per scan.


Automatic Detection Contrast Detection Regression Forest Weill Cornell Medical College Posterior Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Criminisi
    • 1
  • Krishna Juluru
    • 2
  • Sayan Pathak
    • 3
  1. 1.Microsoft Research Ltd.CambridgeUK
  2. 2.Weill Cornell Medical CollegeNew YorkUSA
  3. 3.Microsoft CorporationRedmondUS

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