Fast prospective detection of contrast inflow in X-ray angiograms with convolutional neural network and recurrent neural network
Automatic detection of contrast inflow in X-ray angiographic sequences can facilitate image guidance in computer-assisted cardiac interventions. In this paper, we propose two different approaches for prospective contrast inflow detection. The methods were developed and evaluated to detect contrast frames from X-ray sequences. The first approach trains a convolutional neural network (CNN) to distinguish whether a frame has contrast agent or not. The second method extracts contrast features from images with enhanced vessel structures; the contrast frames are then detected based on changes in the feature curve using long short-term memory (LSTM), a recurrent neural network architecture. Our experiments show that both approaches achieve good performance on detection of the beginning contrast frame from X-ray sequences and are more robust than a state-of-the-art method. As the proposed methods work in prospective settings and run fast, they have the potential of being used in clinical practice.
|Persistent URL||dx.doi.org/10.1007/978-3-319-66179-7_52, hdl.handle.net/1765/101986|
|Series||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Note||e-book, not purchased by EUR|
Ma, H, Ambrosini, P, & van Walsum, T.W. (2017). Fast prospective detection of contrast inflow in X-ray angiograms with convolutional neural network and recurrent neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-319-66179-7_52