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Wan Z, Huang W, Huang S, et al. Coronary Artery Extraction from CT Coronary Angiography with Augmentation on Partially Labelled Data. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:3800-3803doi: 10.1109/EMBC46164.2021.9631094.
Wan, Z., Huang, W., Huang, S., Lu, Z., Zhong, L., & Lin, Z. (2021). Coronary Artery Extraction from CT Coronary Angiography with Augmentation on Partially Labelled Data. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 20213800-3803. https://doi.org/10.1109/EMBC46164.2021.9631094
Wan, Ziqing, et al. "Coronary Artery Extraction from CT Coronary Angiography with Augmentation on Partially Labelled Data." Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference vol. 2021 (2021): 3800-3803. doi: https://doi.org/10.1109/EMBC46164.2021.9631094
Wan Z, Huang W, Huang S, Lu Z, Zhong L, Lin Z. Coronary Artery Extraction from CT Coronary Angiography with Augmentation on Partially Labelled Data. Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3800-3803. doi: 10.1109/EMBC46164.2021.9631094. PMID: 34892063.
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Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3800-3803. doi: 10.1109/EMBC46164.2021.9631094.

Coronary Artery Extraction from CT Coronary Angiography with Augmentation on Partially Labelled Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Ziqing Wan, Weimin Huang, Su Huang, Zhongkang Lu, Liang Zhong, Zhiping Lin

PMID: 34892063 DOI: 10.1109/EMBC46164.2021.9631094

Abstract

Coronary artery disease (CAD) is an important cause of morbidity and mortality. CT coronary angiography is considered as first-line of investigation in patients suspected of having CAD. Coronary artery centerline extraction is a challenging prerequisite for coronary artery stenosis evaluation. These challenges include the small and complex structure, variation of plaques and imaging noise. Deep learning methods often require adequate annotated data to build a good model. This work aims to adopt a dataset that has partial annotation to augment the data to train a Coronary Neural Network (CorNN) to track the coronary artery centerline. We combined a small training dataset with densely labelled centerline and radius, augmented with a larger dataset with only the centerline sparsely labelled to train networks to track centerlines from 3D computed tomography coronary angiography. The vessel orientation estimation is patch based, with or without additional radius prediction. The patch data are carefully positioned and sampled, which are tagged with the orientations computed based on the centerlines. Our experiment results show that, with the augmentation of the new data, although partially annotated, nearly 10% or more improvement has been achieved for the coronary artery extraction by the proposed approach.

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