Utilizing domain knowledge to improve intravenous contrast phase classification of CT scans

Liangchen Liu; Jianfei Liu; Pritam Mukherjee; Akshaya Anand; Yingying Zhu; Bikash Santra; Ronald M. Summers

doi:10.1117/12.3008785

3 April 2024 Utilizing domain knowledge to improve intravenous contrast phase classification of CT scans

Liangchen Liu, Jianfei Liu, Pritam Mukherjee, Akshaya Anand, Yingying Zhu, Bikash Santra, Ronald M. Summers

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271G (2024) https://doi.org/10.1117/12.3008785
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liangchen Liu, Jianfei Liu, Pritam Mukherjee, Akshaya Anand, Yingying Zhu, Bikash Santra, and Ronald M. Summers "Utilizing domain knowledge to improve intravenous contrast phase classification of CT scans", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271G (3 April 2024); https://doi.org/10.1117/12.3008785

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