Abstract
Usually, the directly acquired CT images are from the axial views with respect to the major axes of the body, which do not effectively represent the structure of the heart. If CT imaging is first reformatted into the typical cardiac imaging planes, it will lay the foundation for the subsequent analysis. In this paper, we propose an automatic CT view planning method to acquire standard views of the heart from 3D CT volume, obtaining the equation of the plane by detecting landmarks that can determine this view. To face the challenge of memory cost brought by 3D CT input, we convert the 3D problem into a 2.5D problem, taking into account the spatial context information at the same time. We design a coarse-to-fine framework for the automatic detection of anatomical landmarks. The coarse network is used to estimate the probability distribution of the landmark location in each set of orthogonal planes, and the fine network is further used to regress the offset distance of the current result from the ground-truth. We construct the first known dataset of reformatted cardiac CT with landmark annotations, and the proposed method is evaluated on our dataset, validating its accuracy in the tasks of landmark detection and view planning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Who cardiovascular diseases. https://www.who.int/cardiovascular_diseases/about_cvd/en/. Accessed 29 July 2020
Alansary, A., et al.: Automatic view planning with multi-scale deep reinforcement learning agents. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-LĆ³pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 277ā285. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_32
Blansit, K., Retson, T., Masutani, E., Bahrami, N., Hsiao, A.: Deep learning-based prescription of cardiac MRI planes. Radiol. Artif. Intell. 1(6), e180069 (2019)
De Vos, B.D., Wolterink, J.M., de Jong, P.A., Leiner, T., Viergever, M.A., IÅ”gum, I.: Convnet-based localization of anatomical structures in 3-D medical images. IEEE Trans. Med. Imaging 36(7), 1470ā1481 (2017)
Frick, M., et al.: Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters. J. Magn. Reson. Imaging 34(2), 457ā467 (2011)
Itti, L., Chang, L., Ernst, T.: Automatic scan prescription for brain MRI. Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med. 45(3), 486ā494 (2001)
Jackson, C.E., Robson, M.D., Francis, J.M., Noble, J.A.: Computerised planning of the acquisition of cardiac MR images. Comput. Med. Imaging Graph. 28(7), 411ā418 (2004)
Le, M., Lieman-Sifry, J., Lau, F., Sall, S., Hsiao, A., Golden, D.: Computationally efficient cardiac views projection using 3D convolutional neural networks. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 109ā116. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_13
Lelieveldt, B.P., van der Geest, R.J., Lamb, H.J., Kayser, H.W., Reiber, J.H.: Automated observer-independent acquisition of cardiac short-axis MR images: a pilot study. Radiology 221(2), 537ā542 (2001)
Li, Y., et al.: Fast multiple landmark localisation using a patch-based iterative network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-LĆ³pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 563ā571. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_64
Lu, X., et al.: Automatic view planning for cardiac MRI acquisition. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 479ā486. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_59
McNitt-Gray, M.F.: AAPM/RSNA physics tutorial for residents: topics in CT: radiation dose in CT. Radiographics 22(6), 1541ā1553 (2002)
American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging, et al.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the American Heart Association. Circulation 105(4), 539ā542 (2002)
Noothout, J.M., et al.: Deep learning-based regression and classification for automatic landmark localization in medical images. IEEE Trans. Med. Imaging 39(12), 4011ā4022 (2020)
Noothout, J.M., de Vos, B.D., Wolterink, J.M., Leiner, T., IŔgum, I.: CNN-based landmark detection in cardiac CTA scans. arXiv preprint arXiv:1804.04963 (2018)
NuƱez-Garcia, M., Cedilnik, N., Jia, S., Sermesant, M., Cochet, H.: Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view. In: Puyol Anton, E., et al. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 14ā22. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_2
Payer, C., Å tern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230ā238. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_27
Wei, D., Ma, K., Zheng, Y.: Training automatic view planner forĀ cardiac MR imaging via self-supervision by spatial relationship between views. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 526ā536. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_51
Yang, D., Zhang, S., Yan, Z., Tan, C., Li, K., Metaxas, D.: Automated anatomical landmark detection ondistal femur surface using convolutional neural network. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 17ā21. IEEE (2015)
Zhang, J., Liu, M., Shen, D.: Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans. Image Process. 26(10), 4753ā4764 (2017). https://doi.org/10.1109/TIP.2017.2721106
Zheng, Y., Liu, D., Georgescu, B., Nguyen, H., Comaniciu, D.: 3D deep learning for efficient and robust landmark detection in volumetric data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 565ā572. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_69
Zhou, X., et al.: Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, pp. 256ā261. SPIE (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, X., Zhu, Y. (2022). A 2.5D Coarse-to-Fine Framework forĀ 3D Cardiac CT View Planning. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-18910-4_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18909-8
Online ISBN: 978-3-031-18910-4
eBook Packages: Computer ScienceComputer Science (R0)