Abstract
In the past three years, with the novel use of artificial intelligence for medical image analysis, many authors have focused their efforts on defining automatically the ventricular contours in cardiac Cine MRI. The accuracy reached by deep learning methods is now high enough for routine clinical use. However, integration of other cardiac MR sequences that are routinely acquired along with the functional Cine MR has not been investigated. Namely, T1 maps are static T1-based images that encode in each pixel the T1 relaxation time of the tissue, enabling the definition of local and diffuse fibrosis; T2 maps are static T2-based images that highlight excess water (edema) within the muscle; Late Gadolinium Enhancement (LGE) images are acquired 10 min after injection of a contrast agent that will linger in infarct areas. These sequences are acquired in short-axis plane similar to the 2D Cine MRI, and therefore contain similar anatomical features. In this paper we focus on segmenting the left ventricle in these structural images for further physiological quantification. We first evaluate the use of transfer learning from a model trained on Cine data to analyze these short-axis structural sequences. We also develop an automatic slice selection method to avoid over-segmentation which can be critical in scar/fibrosis/edema delineation. We report good accuracy with dice scores around 0.9 for T1 and T2 maps and correlation of the physiological parameters above 0.9 using only 40 scans and executed in less than 15 s on CPU.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baumgartner, C.F., Koch, L.M., Pollefeys, M., Konukoglu, E.: An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 111–119. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_12
Ciofolo, C., Fradkin, M., Mory, B., Hautvast, G., Breeuwer, M.: Automatic myocardium segmentation in late-enhancement MRI. In: ISBI 2008, pp. 225–228. IEEE (2008)
Flett, A.S., et al.: Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. JACC Cardiovasc. Imaging. 4(2), 150–156 (2011)
Isensee, F., Jaeger, P.F., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H.: Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 120–129. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_13
Liu, J., Li, W., Tian, Y.: Automatic thresholding of gray-level pictures using two-dimension Otsu method. In: International Conference on Circuits and Systems, pp. 325–327. IEEE (1991)
Khened, M., Alex, V., Krishnamurthi, G.: Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 140–151. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_15
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
McAlindon, E., Pufulete, M., Lawton, C., Angelini, G.D., Bucciarelli-Ducci, C.: Quantification of infarct size and myocardium at risk: evaluation of different techniques and its implications. EHJ-CVI 16(7), 738–746 (2015)
Paknezhad, M., Marchesseau, S., Brown, M.S.: Automatic basal slice detection for cardiac analysis. J. Med. Imaging 3(3) (2016). https://doi.org/10.1117/1.JMI.3.3.034004
Pop, M., Sermesant, M., Jodoin, P.-M., Lalande, A., Zhuang, X., Yang, G., Young, A., Bernard, O. (eds.): STACOM 2017. LNCS, vol. 10663. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0
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
Wei, D., Sun, Y., Chai, P., Low, A., Ong, S.H.: Myocardial segmentation of late Gadolinium enhanced MR images by propagation of contours from cine MR images. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 428–435. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_53
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
Fadil, H., Totman, J.J., Marchesseau, S. (2019). Deep Learning Segmentation of the Left Ventricle in Structural CMR: Towards a Fully Automatic Multi-scan Analysis. 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_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-12029-0_5
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)