Abstract
Cardiac magnetic resonance imaging (CMRI) provides high resolution images ideal for assessing cardiac function and diagnosis of cardiovascular diseases. To assess cardiac function, estimation of ejection fraction, ventricular volume, mass and stroke volume are crucial, and the segmentation of left ventricle from CMRI is the first critical step. Fully convolutional neural network architectures have proved to be very efficient for medical image segmentation, with U-Net inspired architecture as the current state-of-the-art. Generative adversarial networks (GAN) inspired architectures have recently gained popularity in medical image segmentation with one of them being SegAN, a novel end-to-end adversarial neural network architecture. In this paper, we investigate SegAN with three different types of U-Net inspired architectures for left ventricle segmentation from cardiac MRI data. We performed our experiments on the 2017 ACDC segmentation challenge dataset. Our results show that the performance of U-Net architectures is better when trained in the SegAN framework than when trained stand-alone. The mean Dice scores achieved for three different U-Net architectures trained in the SegAN framework was on the order of 93.62%, 92.49% and 94.57%, showing a significant improvement over their Dice scores following stand-alone training - 92.58%, 91.46% and 93.81%, respectively.
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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
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Haering, M., Grosshans, J., Wolf, F., Eule, S.: Automated segmentation of epithelial tissue using cycle-consistent generative adversarial networks. bioRxiv p. 311373 (2018)
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., Sermesant, M., Jodoin, P.-M., Lalande, A., Zhuang, X., Yang, G., Young, A., Bernard, O. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 120–129. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_13
La, A.G., et al.: Cardiac MRI: a new gold standard for ventricular volume quantification during high-intensity exercise. Circ. Cardiovasc. Imaging 6(2), 329–338 (2013)
Lin, X., Cowan, B., Young, A.: Model-based graph cut method for segmentation of the left ventricle. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 3059–3062. IEEE (2006)
Nasr-Esfahani, M., et al.: Left ventricle segmentation in cardiac MR images using fully convolutional network. arXiv preprint arXiv:1802.07778 (2018)
Petitjean, C., et al.: Right ventricle segmentation from cardiac MRI: a collation study. Med. Image Anal. 19(1), 187–202 (2015)
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
Santiago, C., Nascimento, J.C., Marques, J.S.: A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes. Neural Comput. Appl. 28(9), 2489–2500 (2017)
Suinesiaputra, A., et al.: A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Med. Image Anal. 18(1), 50–62 (2014)
Suinesiaputra, A., et al.: Left ventricular segmentation challenge from cardiac MRI: a collation study. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2011. LNCS, vol. 7085, pp. 88–97. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28326-0_9
White, H.D., Norris, R.M., Brown, M.A., Brandt, P.W., Whitlock, R., Wild, C.J.: Left ventricular end-systolic volume as the major determinant of survival after recovery from myocardial infarction. Circulation 76(1), 44–51 (1987)
Xue, Y., Xu, T., Zhang, H., Long, L.R., Huang, X.: Segan: adversarial network with multi-scale l 1 loss for medical image segmentation. Neuroinformatics 16, 383–392 (2018)
Acknowledgement
Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award No. R35GM128877 and by the Office of Advanced Cyber-infrastructure of the National Science Foundation under Award No. 1808530.
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Upendra, R.R., Dangi, S., Linte, C.A. (2019). An Adversarial Network Architecture Using 2D U-Net Models for Segmentation of Left Ventricle from Cine Cardiac MRI. In: Coudière, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2019. Lecture Notes in Computer Science(), vol 11504. Springer, Cham. https://doi.org/10.1007/978-3-030-21949-9_45
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