Histogram-Based Discrimination of Intravenous Contrast in Abdominopelvic Computed Tomography

The aim of this study was to evaluate the accuracy of fully automated machine learning methods for detecting intravenous contrast in computed tomography (CT) studies of the abdomen and pelvis.

A set of 591 labeled CT image volumes of the abdomen and pelvis was obtained from 5 different CT scanners, of which 434 (73%) were performed with intravenous contrast. A stratified split of this set was performed into training and test sets of 443 and 148 studies, respectively. Subsequently, support vector machine and logistic regression classifiers were trained using 5-fold cross-validation for parameter optimization.

The best in-sample performance was seen with a support vector machine classifier with a χ kernel (98.9% accuracy); however, test set performance was similar across the trained classifiers, with 95% to 97% accuracy.

Histogram-based automated classifiers for the presence of intravenous contrast are accurate and may be useful for verifying the accurate labeling of the presence of intravenous contrast in CT body studies.