Abstract
4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in order to compute the cardiac views. Our approach consists of first calculating a bounding box that tightly crops the heart, followed by a landmark localization step within this bounded region. Both steps are based on a 3D extension of the recently introduced ENet. We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist.
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
Payer, C., Štern, 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. 230–238. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_27
Ghesu, F.C., Georgescu, B., Mansi, T., Neumann, D., Hornegger, J., Comaniciu, D.: An artificial agent for anatomical landmark detection in medical images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 229–237. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_27
Lu, X., Jolly, M.-P., Georgescu, B., Hayes, C., Speier, P., Schmidt, M., Bi, X., Kroeker, R., Comaniciu, D., Kellman, P., Mueller, E., Guehring, J.: Automatic view planning for Cardiac MRI acquisition. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 479–486. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23626-6_59
Pfister, T., Charles, J., Zisserman, A.: Flowing convnets for human pose estimation in videos. In: ICCV, pp. 1913–1921 (2015)
Paszke, A., et al.: Enet: a deep neural network architecture for real-time semantic segmentation arXiv:1606.02147 (2016)
Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics gems IV. Academic Press Professional Inc., pp. 474–485 (1994)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE CVPR, pp. 770–778 (2016)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions arXiv:1511.07122 (2015)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization arXiv:1412.6980 (2014)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network arXiv:1612.01105 (2016)
Ilankathir, S.: A cadaveric study on adult human heart valve annular circumference and its clinical significance. IOSR-JDMS 1(14), 60–64 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Le, M., Lieman-Sifry, J., Lau, F., Sall, S., Hsiao, A., Golden, D. (2017). Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-67558-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67557-2
Online ISBN: 978-3-319-67558-9
eBook Packages: Computer ScienceComputer Science (R0)