Automatic Contrast Phase Detection on Abdominal Computed Tomography using Clinically-Inspired Techniques

Eduardo Pontes Reis, Louis Blankemeier, Juan Manuel Zambrano Chaves, Malte Jensen, Sally Yao, Cesar Augusto Madid Truyts, Marc H. Willis, Robert D. Boutin, Edson Amaro Jr, Akshay S Chaudhari

Keywords: Abdominal CT Scan, Contrast Phase, Organ Segmentation, Radiology, Med- ical Imaging, Machine Learning, Artificial Intelligence

TL;DR: We present an open-source algorithm for automatic contrast phase detection in abdominal CT scans, using clinically inspired anatomical structure segmentation and a gradient boosting classifier, facilitating AI applications in medical imaging.

Abstract: 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.