Abstract
Coronary artery segmentation on coronary-computed tomography angiography (CCTA) images is crucial for clinical use. Due to the expertise-required and labor-intensive annotation process, there is a growing demand for the relevant label-efficient learning algorithms. To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary artery segmentation and clinical diagnostic characteristics. Further, we propose a progressive weakly supervised learning framework to achieve accurate segmentation under PVA. First, our proposed framework learns the local features of vessels to propagate the knowledge to unlabeled regions. Subsequently, it learns the global structure by utilizing the propagated knowledge, and corrects the errors introduced in the propagation process. Finally, it leverages the similarity between feature embeddings and the feature prototype to enhance testing outputs. Experiments on clinical data reveals that our proposed framework outperforms the competing methods under PVA (24.29% vessels), and achieves comparable performance in trunk continuity with the baseline model using full annotation (100% vessels).
Z. Zhang and X. Zhang—Contributed equally to this work.
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
Similar content being viewed by others
References
Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint: arXiv:2102.04306 (2021)
Cheng, H.T., et al.: Self-similarity student for partial label histopathology image segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 117–132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_8
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Gharleghi, R., Chen, N., Sowmya, A., Beier, S.: Towards automated coronary artery segmentation: a systematic review. Comput. Methods Programs Biomed., 107015 (2022)
He, Y., et al.: Learning better registration to learn better few-shot medical image segmentation: Authenticity, diversity, and robustness. IEEE Trans. Neural Netw. Learn. Syst. (2022)
He, Y., et al.: Dense biased networks with deep priori anatomy and hard region adaptation: semi-supervised learning for fine renal artery segmentation. Med. Image Anal. 63, 101722 (2020)
Ho, D.J., et al.: Deep multi-magnification networks for multi-class breast cancer image segmentation. Comput. Med. Imaging Graph. 88, 101866 (2021)
Kirişli, H., et al.: Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med. Image Anal. 17(8), 859–876 (2013)
Lee, H., Jeong, W.-K.: Scribble2Label: scribble-supervised cell segmentation via self-generating pseudo-labels with consistency. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 14–23. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_2
Luo, X., et al.: Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. Lecture Notes in Computer Science, vol. 13431. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_50
Nguyen, D.T., Mummadi, C.K., Ngo, T.P.N., Nguyen, T.H.P., Beggel, L., Brox, T.: Self: Learning to filter noisy labels with self-ensembling. arXiv preprint: arXiv:1910.01842 (2019)
Peng, L., et al.: Semi-supervised learning for semantic segmentation of emphysema with partial annotations. IEEE J. Biomed. Health Inform. 24(8), 2327–2336 (2019)
Qi, Y., et al.: Examinee-examiner network: weakly supervised accurate coronary lumen segmentation using centerline constraint. IEEE Trans. Image Process. 30, 9429–9441 (2021)
Schaap, M., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med. Image Anal. 13(5), 701–714 (2009)
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)
Shen, W., et al.: A survey on label-efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction. IEEE Trans. Pattern Anal. Mach. Intell., 1–20 (2023). https://doi.org/10.1109/TPAMI.2023.3246102
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)
Zhai, S., Wang, G., Luo, X., Yue, Q., Li, K., Zhang, S.: PA-Seg: learning from point annotations for 3D medical image segmentation using contextual regularization and cross knowledge distillation. IEEE Trans. Med. Imaging 42, 2235–2246 (2023). https://doi.org/10.1109/TMI.2023.3245068
Zhang, J., Gu, R., Wang, G., Xie, H., Gu, L.: SS-CADA: a semi-supervised cross-anatomy domain adaptation for coronary artery segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1227–1231. IEEE (2021)
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, Y., Li, Z., Xie, Y., Qu, Y., Li, C., Mei, T.: Weakly supervised semantic segmentation for large-scale point cloud. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3421–3429 (2021)
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), pp. 1–6. IEEE (2022)
Acknowledgements.
This research was supported by the Intergovernmental Cooperation Project of the National Key Research and Development Program of China(2022YFE0116700). We thank the Big Data Computing Center of Southeast University for providing the facility support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Z., Zhang, X., Qi, Y., Yang, G. (2023). Partial Vessels Annotation-Based Coronary Artery Segmentation with Self-training and Prototype Learning. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-43895-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43894-3
Online ISBN: 978-3-031-43895-0
eBook Packages: Computer ScienceComputer Science (R0)
