PurposeAutomatic comprehensive reporting of coronary artery disease (CAD) requires anatomical localization of the coronary artery pathologies. To address this, we propose a fully automatic method for extraction and anatomical labeling of the coronary artery tree using deep learning.ApproachWe include coronary CT angiography (CCTA) scans of 104 patients from two hospitals. Reference annotations of coronary artery tree centerlines and labels of coronary artery segments were assigned to 10 segment classes following the American Heart Association guidelines. Our automatic method first extracts the coronary artery tree from CCTA, automatically placing a large number of seed points and simultaneous tracking of vessel-like structures from these points. Thereafter, the extracted tree is refined to retain coronary arteries only, which are subsequently labeled with a multi-resolution ensemble of graph convolutional neural networks that combine geometrical and image intensity information from adjacent segments.ResultsThe method is evaluated on its ability to extract the coronary tree and to label its segments, by comparing the automatically derived and the reference labels. A separate assessment of tree extraction yielded an F1 score of 0.85. Evaluation of our combined method leads to an average F1 score of 0.74.ConclusionsThe results demonstrate that our method enables fully automatic extraction and anatomical labeling of coronary artery trees from CCTA scans. Therefore, it has the potential to facilitate detailed automatic reporting of CAD.
Invasive treatment of coronary artery disease (CAD) is costly and burdensome for the patient. Therefore, prediction of treatment success would be of clinical value. This study presents a deep learning method for the prediction of the post-treatment fractional flow reserve (FFR) in patients with coronary artery disease (CAD) from pre-treatment coronary CT angiography (CCTA). To simulate post-treatment FFR, pre-treatment coronary artery characteristics are modified to mimic invasive coronary treatment. Artery characterization and subsequent prediction of the FFR values along the artery are performed using deep learning. The method was tested on CCTA scans of 29 patients with invasive pre- and post-treatment FFR measurements along the artery. Achieved accuracy for the prediction of the presence of a functionally significant stenosis was 0.77 before and 0.63 after simulated treatment. The analysis took 0.8 s per artery. The results indicate that real-time treatment planning might be feasible.
Accurately labeled segments of the coronary artery trees are important for diagnostic reporting of coronary artery disease. As current automatic reporting tools do not consider anatomical segment labels, accurate automatic solutions for deriving these labels would be of great value. We propose an automatic method for labeling segments in coronary artery trees represented by centerlines automatically extracted from CCTA images. Using the connectivity between the centerlines, we construct a tree graph. Coronary artery segments are defined as edges of this graph and characterized by location and geometry features. The constructed coronary artery tree is transformed into a linegraph and used as input to a graph attention network, which is trained to classify labels of coronary artery segments. The method was evaluated on 71 CCTA images, achieving an F1-score of 92.4% averaged over all patients and segments. The results indicate that graph attention networks are suitable for coronary artery tree labeling.
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