Automatic Switching of Organ Programs in Interventional X-ray Machines Using Deep Learning

Zusammenfassung

In interventional radiology, the optimal parametrization of the X-ray image and any subsequent software processing strongly depends on the body region being imaged. These anatomy-specific parameters are combined to create customized organ programs and are necessary to obtain an optimal image quality. In today’s workflow, these programs have to be switched manually by the surgeon, which can be complex. This paper investigates a deep learning algorithm for automatic switching of organ programs in interventional X-ray machines based on the automatic detection of the imaged anatomy. We compare multiple network architectures for cardiac anatomy classification where the algorithm has to differentiate the left coronary artery, right coronary artery, and left ventricle on radiographs without contrast medium. The best-performing model achieves a micro average F1-score of 0.80. A comparison of the model performance with expert rater annotations shows promising results and recommends further clinical evaluation.