Abstract
Coronary artery segmentation is a critical yet challenging step in coronary artery stenosis diagnosis. Most existing studies ignore important contextual anatomical information and vascular topologies, leading to limited performance. To this end, this paper proposes a progressive deep-learning based framework for accurate coronary artery segmentation by leveraging contextual anatomical information and vascular topologies. The proposed framework consists of a spatial anatomical dependency (SAD) module and a hierarchical topology learning (HTL) module. Specifically, the SAD module coarsely segments heart chambers and coronary artery for region proposals, and captures spatial relationship between coronary artery and heart chambers. Then, the HTL module adopts a multi-task learning mechanism to improve the coarse coronary artery segmentation by simultaneously predicting the hierarchical vascular topologies i.e., key points, centerlines, and neighboring cube-connectivity. Extensive evaluations, ablation studies, and comparisons with existing methods show that our method achieves state-of-the-art segmentation performance.
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References
Cao, D., et al.: Non-cardiac surgery in patients with coronary artery disease: risk evaluation and periprocedural management. Nat. Rev. Cardiol. 18(1), 37–57 (2021)
Collet, C., et al.: Left main coronary artery disease: pathophysiology, diagnosis, and treatment. Nat. Rev. Cardiol. 15(6), 321–331 (2018)
Hao, D., et al.: Sequential vessel segmentation via deep channel attention network. Neural Netw. 128, 172–187 (2020)
Huang, K., et al.: Coronary wall segmentation in CCTA scans via a hybrid net with contours regularization. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1743–1747. IEEE (2020)
Kampffmeyer, M., Dong, N., Liang, X., Zhang, Y., Xing, E.P.: ConnNet: a long-range relation-aware pixel-connectivity network for salient segmentation. IEEE Trans. Image Process. 28(5), 2518–2529 (2018)
Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-ventricle quantification using residual U-Net. In: Pop, M., Sermesant, M., Zhao, J., Li, S., McLeod, K., Young, A., Rhode, K., Mansi, T. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 371–380. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_40
Marano, R., et al.: CCTA in the diagnosis of coronary artery disease. Radiol. Med. (Torino) 125(11), 1102–1113 (2020). https://doi.org/10.1007/s11547-020-01283-y
Mou, L., Zhao, Y., Fu, H., Liu, Y., Cheng, J., Zheng, Y., Su, P., Yang, J., Chen, L., Frangi, A.F., et al.: \(\rm CS^{2}\)-Net: deep learning segmentation of curvilinear structures in medical imaging. Med. Image Anal. 67, 101874 (2021)
Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., Shen, D.: 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Trans. Cybern. 49(3), 1123–1136 (2018)
Qi, Y., et al.: Examinee-Examiner Network: weakly supervised accurate coronary lumen segmentation using centerline constraint. IEEE Trans. Image Process. 30, 9429–9441 (2021)
Qin, Y., et al.: AirwayNet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 212–220. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_24
Serruys, P.W., et al.: Coronary computed tomographic angiography for complete assessment of coronary artery disease: JACC state-of-the-art review. J. Am. Coll. Cardiol. 78(7), 713–736 (2021)
Shit, S., et al.: clDice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560–16569 (2021)
Virani, S.S., Alonso, A., Aparicio, H.J., et al.: Heart disease and stroke statistics-2021 update: a report from the American Heart Association. Circulation 143(8), e254–e743 (2021)
Xiang, L., et al.: Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image. Med. Image Anal. 47, 31–44 (2018)
Zhang, J., et al.: Weakly supervised vessel segmentation in X-ray angiograms by self-paced learning from noisy labels with suggestive annotation. Neurocomputing 417, 114–127 (2020)
Zhang, X., Cui, Z., Feng, J., Song, Y., Wu, D., Shen, D.: CorLab-Net: anatomical dependency-aware point-cloud learning for automatic labeling of coronary arteries. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds.) MLMI 2021. LNCS, vol. 12966, pp. 576–585. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87589-3_59
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Zhu, X., Cheng, Z., Wang, S., Chen, X., Lu, G.: Coronary angiography image segmentation based on PSPNet. Comput. Methods Programs Biomed. 200, 105897 (2021)
Zreik, M., et al.: Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography. IEEE Trans. Med. Imaging 39(5), 1545–1557 (2020)
Acknowledgment
This work was supported in part by National Natural Science Foundation of China (grant number 62131015, 62073260), and Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600).
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Zhang, X. et al. (2022). Progressive Deep Segmentation of Coronary Artery via Hierarchical Topology Learning. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_38
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