Abstract
Coronary artery centerlines extraction from cardiac CT angiography (CCTA) is an important but challenging task. The popular U-net based coronary artery segmentation and thinning approaches rely on large number of labeled data and tend to produce noisy results. We proposed a graph convolutional network (GCN) for refining noisy centerlines outputted by U-net and developed a coronary artery tree synthesis approach for GCN pretraining. Experiments demonstrate that both modules led to improved performance.
This work was supported in part by the National Natural Science Foundation of China under Grants 61976121 and 82071921.
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Guo, Z., Zhang, Y., Feng, J., Yang, E., Qin, L., Zhou, J. (2022). Coronary Artery Centerline Refinement Using GCN Trained with Synthetic Data. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_3
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