Abstract
Cardiovascular diseases are among the leading causes of death globally. Cardiac left ventricle (LV) quantification is known to be one of the most important tasks for the identification and diagnosis of such pathologies. In this paper, we propose a deep learning method that incorporates 3D spatio-temporal convolutions to perform direct left ventricle quantification from cardiac MR sequences. Instead of analysing slices independently, we process stacks of temporally adjacent slices by means of 3D convolutional kernels which fuse the spatio-temporal information, incorporating the temporal dynamics of the heart to the learned model. We show that incorporating such information by means of spatio-temporal convolutions into standard LV quantification architectures improves the accuracy of the predictions when compared with single-slice models, achieving competitive results for all cardiac indices and significantly breaking the state of the art [10] for cardiac phase estimation.
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Notes
- 1.
LVQuan Challenge website: https://lvquan18.github.io/.
- 2.
The source code for the proposed architecture is publicly available at https://github.com/alejandrodebus/SpatioTemporalCNN_lvquan.
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Acknowledgements
The present work used computational resources of the Pirayu Cluster, acquired with funds from the Santa Fe Science, Technology and Innovation Agency (ASACTEI), Government of the Province of Santa Fe, through Project AC-00010-18, Resolution No. 117/14. This equipment is part of the National System of High Performance Computing of the Ministry of Science, Technology and Productive Innovation of the Republic of Argentina. We also thank NVidia for the donation of a GPU used for this project. Enzo Ferrante is a beneficiary of an AXA Research Fund grant.
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Debus, A., Ferrante, E. (2019). Left Ventricle Quantification Through Spatio-Temporal CNNs. 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_50
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