Abstract
Accurate extraction of coronary arteries from coronary computed tomography angiography (CCTA) is a prerequisite for the computer-aided diagnosis of coronary artery disease (CAD). Deep learning-based methods can achieve automatic segmentation of vasculatures, but few of them focus on the connectivity and completeness of the coronary tree. In this paper, we propose CorSegRec, a topology-preserving scheme for extracting fully-connected coronary artery, which integrates image segmentation, centerline reconnection, and geometry reconstruction. First, we employ a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we propose a regularized walk algorithm, by integrating distance, probabilities predicted by centerline classifier, and cosine similarity to reconnect centerlines. Third, we apply level-set segmentation and implicit modeling techniques to reconstruct the geometric model of the missing vessels. Experiment results on two datasets demonstrate that the proposed method outperforms other methods with better volumetric scores and higher vascular connectivity. Code will be available at https://github.com/YH-Qiu/CorSegRec.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Banh, D., Kyprianou, I.S., Paquerault, S., Myers, K.J.: Morphology-based three-dimensional segmentation of coronary artery tree from CTA scans. In: Medical Imaging (2007)
Bock, S., Giger, M.L., Karssemeijer, N., Kühnel, C., Boskamp, T., Peitgen, H.O.: Robust vessel segmentation. In: Proceedings of SPIE - The International Society for Optical Engineering 2013, pp. 691539–691539-9 (2008)
Fu, L., Kang, Y., Zhu, Z.: Centerline correction of incorrectly segmented coronary arteries in CT angiography. Proc. SPIE 8768, 87683G (2013)
Gharleghi, R., et al.: Automated segmentation of normal and diseased coronary arteries - the ASOCA challenge. Comput. Med. Imaging Graph. 97, 102049 (2022). https://www.sciencedirect.com/science/article/pii/S0895611122000222
Gharleghi, R., et al.: Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteries (2022). https://arxiv.org/abs/2211.01859
Han, D., Shim, H., Jeon, B.: Automatic coronary artery segmentation using active search for branches and seemingly disconnected vessel segments from coronary CT angiography. PLoS ONE 11(8), e0156837 (2016)
Han, K., et al.: Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images (2021)
Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2020)
Li, M., et al.: Deep learning segmentation and reconstruction for CT of chronic total coronary occlusion. Radiology 306, 221393 (2022)
Li, Q., Tian, J.: Partial shape-preserving splines. Comput. Aided Des. 43(4), 394–409 (2011)
M’Hiri, F., Duong, L., Desrosiers, C., Cheriet, M.: VesselWalker: coronary arteries segmentation using random walks and Hessian-based vesselness filter. In: IEEE International Symposium on Biomedical Imaging (2013)
Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-ventricle quantification using residual U-Net. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 371–380. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_40
Mou, L., Chen, L., Cheng, J., Gu, Z., Zhao, Y., Liu, J.: Dense dilated network with probability regularized walk for vessel detection. IEEE Trans. Med. Imaging 39(5), 1392–1403 (2019)
Mou, L., et al.: CS2-Net: deep learning segmentation of curvilinear structures in medical imaging. Elsevier (2021)
Roth, G.A., et al.: Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J. Am. Coll. Cardiol. 76, 2982 (2020). (15), 77 (2021)
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: Computer Vision and Pattern Recognition (2021)
Wang, Q., et al.: Geometric morphology based irrelevant vessels removal for accurate coronary artery segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 757–760 (2021)
Wolterink, J.M., Leiner, T., Išgum, I.: Graph convolutional networks for coronary artery segmentation in cardiac CT angiography. In: Zhang, D., Zhou, L., Jie, B., Liu, M. (eds.) GLMI 2019. LNCS, vol. 11849, pp. 62–69. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35817-4_8
Zhang, X., et al.: Progressive deep segmentation of coronary artery via hierarchical topology learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2022)
Zheng, Y., Wang, B., Hong, Q.: UGAN: semi-supervised medical image segmentation using generative adversarial network. In: 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (2022)
Zhou, Z.H., Feng, J.: Deep forest. Natl. Sci. Rev. 6(1), 74–86 (2019)
Zhu, X., Cheng, Z., Wang, S., Chen, X., Lu, G.: Coronary angiography image segmentation based on PSPNet. Comput. Methods Programs Biomed. 200(4), 105897 (2020)
Acknowledgments
This work was supported in part by the Natural Science Foundation of Fujian Province of China (No. 2020J01006), the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2022AC04), National Natural Science Foundation of China (No. 62131015), Beijing Natural Science Foundation (No. Z210013), and ITC-InnoHK Projects at COCHE.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Qiu, Y. et al. (2023). CorSegRec: A Topology-Preserving Scheme for Extracting Fully-Connected Coronary Arteries from CT Angiography. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_64
Download citation
DOI: https://doi.org/10.1007/978-3-031-43898-1_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43897-4
Online ISBN: 978-3-031-43898-1
eBook Packages: Computer ScienceComputer Science (R0)
