Intravenous contrast phase classification can improve the data curation of CT scans for medical AI applications. In our early work, a five-phase classification model based on ResNet was developed to serve this purpose. It can accurately predict CT phases if the testing data follows the data distribution of the training data, while its performance drops substantially on the out-of-distribution data. To address this issue, this work aims to incorporate the inherent domain knowledge related to different CT phases for their classification. We explore the intensity distributions of a few key organs in different CT phases. TotalSegmentator was used to segment these organs including pulmonary artery, heart atrium, heart ventricle, heart myocardium, aorta, portal vein, splenic vein, liver, kidney, inferior vena cava, iliac artery, iliac veins, and urinary bladder. The intensity information was then extracted as image features to train a random forest classification model. The classification models were trained on an in-house dataset of 252 CT scans. They were validated on a testing dataset with 213 outof-distribution CT scans. The proposed method achieved better accuracy of 62.44%, while it was 53.02% using ResNet. These results showed that embedding domain knowledge could improve CT phase classification to be robust to the out-of-distribution dataset.
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