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Accurately determining contrast phase in an abdominal computed tomography (CT) series is an important step prior to deploying downstream artificial intelligence methods trained to operateon the specific series. Inspired by how radiologists assess contrast phase status, this paper presents a simple approach to automatically detect the contrast phase. This method combines features extracted from the segmentation of key anatomical structures with a gradient boosting classifier for this task. The algorithm demonstrates high accuracy in categorizing the images into non-contrast (96.6\% F1 score), arterial (78.9\% F1 score), venous (92.2\% F1 score), and delayed phases (95.0\% F1 score), making it a valuable tool for enhancing AI applicability in medical imaging.
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