Abstract
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooling) layers, which in turn reduces segmentation accuracy for smaller structures. In this paper, we propose a 3D CNN for volumetric segmentation of the left atrial chamber in LGE-MRI. Our network is based on the well known U-Net architecture. We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge. The results show that including global context through the use of dilated convolutions, helps in domain adaptation, and the overall segmentation accuracy is improved in comparison to a 3D U-Net.
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
Guang, Y., et al.: Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI. Med. Phys. 45(4), 1562–1576 (2018)
McGann, C., et al.: Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI. Circ.: Arrhythm. Electrophysiol. 7(1), 23–30 (2014)
Zhao, J., et al.: Three-dimensional integrated functional, structural, and computational mapping to define the structural “fingerprints” of heart-specific atrial fibrillation drivers in human heart ex vivo. J. Am. Heart Assoc. 6(8), e005922 (2017)
Oakes, R.S., et al.: Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation 119(13), 1758–1767 (2009)
Tao, Q., Ipek, E.G., Shahzad, R., Berendsen, F.F., Nazarian, S., van der Geest, R.J.: Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: towards objective atrial scar assessment. J. Magn. Reson. Imaging 44(2), 346–354 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571, October 2016
Mortazi, A., Karim, R., Rhode, K., Burt, J., Bagci, U.: CardiacNET: segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 377–385. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_43
Jin, C., et al.: Left atrial appendage segmentation using fully convolutional neural networks and modified three-dimensional conditional random fields. IEEE J. Biomed. Health Inform. 1 (2018)
Kurzendorfer, T., Forman, C., Schmidt, M., Tillmanns, C., Maier, A., Brost, A.: Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI. Comput. Med. Imaging Graph. 59, 13–27 (2017)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2015)
Xia, Q., Hao, Y., Hu, Z., Hao, A.: Automatic 3D atrial segmentation from gadolinium-enhanced MRI using volumetric fully convolutional networks. In: Statistical Atlases and Computational Models of the Heart, STACOM 2018. Springer, Heidelberg (2018)
Bian, C., et al.: Pyramid network with online hard example mining for accurate left atrium segmentation. In: Statistical Atlases and Computational Models of the Heart, STACOM 2018. Springer, Heidelberg (2018)
Li, C., et al.: Attention based hierarchical aggregation network for 3D left atrial segmentation. In: Statistical Atlases and Computational Models of the Heart, STACOM 2018. Springer, Heidelberg (2018)
Puybareau, E., Zhou, Z., Khoudli, Y., Xu, Y., Lacotte, J., Géraud, T.: Left atrial segmentation in a few seconds using fully convolutional network and transfer learning. In: Statistical Atlases and Computational Models of the Heart, STACOM 2018. Springer, Heidelberg (2018)
Yang, X., et al.: Combating uncertainty with novel losses for automatic atrium segmentation. In: Statistical Atlases and Computational Models of the Heart, STACOM 2018. Springer, Heidelberg (2018)
Acknowledgements
This study was partially supported by the project BIG-THERA: Integrative ‘Big Data Modeling’ for the development of novel therapeutic approaches for breast cancer, and the BMBF grant: Using big data to advance breast cancer research. Radiogenomics in breast cancer: Combining imaging and genomic data to address breast cancer complexity. The authors would also like to thank NVIDIA for donating a Titan XP GPU.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Vesal, S., Ravikumar, N., Maier, A. (2019). Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-12029-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12028-3
Online ISBN: 978-3-030-12029-0
eBook Packages: Computer ScienceComputer Science (R0)