Abstract
Estimating dimensional measurements of the left ventricle provides diagnostic values which can be used to assess cardiac health and identify certain pathologies. In this paper we describe our methodology of calculating measurements from left ventricle segmentations automatically generated using deep learning. We use a U-net convolutional neural network architecture built from residual units to segment the left ventricle and then process these segmentations to estimate the area of the cavity and myocardium, the dimensions of the cavity, and the thickness of the myocardium. Determining if an image is part of the diastolic or systolic portion of the cardiac cycle is done by analysing the cavity volume. The quality of our results are dependent on our training regime where we have generated a large derivative dataset by augmenting the original images with free-form deformations. Our expanded training set, in conjunction with simple affine image transforms, creates a sufficiently large training population to prevent over-fitting of the network while still creating an accurate and robust segmentation network. Assessing our method on the STACOM18 LVQuan challenge dataset we find that it significantly outperforms the previously published state-of-the-art on a 5-fold validation all tasks considered.
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
Avendi, M., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)
Bai, W., et al.: Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovasc. Magn. Reson. 20, 65 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imageNet classification. CoRR abs/arXiv:1502.0185 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. CoRR abs/1603.05027 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 1097–1105. Curran Associates Inc., USA (2012)
Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384–395 (2018)
Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S.E., Frangi, A.F.: A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magn. Reson. Mater. Phys. Biol. Med. 29(2), 155–195 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015)
Simard, P.Y., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. Institute of Electrical and Electronics Engineers, Inc. August 2003
Suinesiaputra, A., et al.: Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours. J. Cardiovasc. Magn. Reson. 17(1), 63 (2015)
Tan, L.K., Liew, Y.M., Lim, E., McLaughlin, R.A.: Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences. Med. Image Anal. 39, 78–86 (2017)
Tran, P.V.: A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494 (2016)
Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Instance normalization: The missing ingredient for fast stylization. CoRR abs/1607.08022 (2016). http://arxiv.org/abs/1607.08022
Wang, Z., Salah, M.B., Gu, B., Islam, A., Goela, A., Li, S.: Direct estimation of cardiac biventricular volumes with an adapted bayesian formulation. IEEE Trans. Biomed. Eng. 61(4), 1251–1260 (2014)
Xue, W., Brahm, G., Pandey, S., Leung, S., Li, S.: Full left ventricle quantification via deep multitask relationships learning. Med. Image Anal. 43, 54–65 (2018)
Xue, W., Islam, A., Bhaduri, M., Li, S.: Direct multitype cardiac indices estimation via joint representation and regression learning. IEEE Trans. Med. Imaging 36(10), 2057–2067 (2017)
Xue, W., Nachum, I.B., Pandey, S., Warrington, J., Leung, S., Li, S.: Direct estimation of regional wall thicknesses via residual recurrent neural network. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 505–516. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_40
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-Net. CoRR abs/1711.10684 (2017)
Acknowledgments
This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z). The GPUs used in this research was generously donated by the NVIDIA Corporation.
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
Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A. (2019). Left-Ventricle Quantification Using Residual U-Net. 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_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-12029-0_40
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)