Abstract
Segmentation of anatomical structures in cardiac MR images is an important problem because it is necessary for evaluation of morphology of these structures for diagnostic purposes. Automatic segmentation algorithm with near-human accuracy would be extremely helpful for a medical specialist. In this paper we consider such structures as endocardium and epicardium of right ventricle. We compare the performance of the best existing neural networks such as U-Net and GridNet, and propose our own modification of U-Net which implies replacement of every second convolution layer with dilated (atrous) convolution layer. Evaluation on benchmark dataset RVSC demonstrated that the proposed algorithm allows to improve the segmentation accuracy up to 6% both for endocardium and epicardium compared to original U-Net. The algorithm also overperforms GridNet for both segmentation problems.
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
References
Ringenberg, J., Deo, M., Devabhaktuni, V., et al.: Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI. Comput. Med. Imag. Grap. 38(3), 190–201 (2014)
Punithakumar, K., Noga, M., Ben Ayed, I., Boulanger, P.: Right ventricular segmentation in cardiac MRI with moving mesh correspondences. Comput. Med. Imag. Grap. 43, 15–25 (2015)
Zhen, X., Wang, Z., Islam, A., et al.: Multiscale deep networks and regression forests for direct biventricular volume estimation. Med. Image Anal. 30, 120–129 (2016)
Luo, G., An, R., Wang, K., et al.: A deep learning network for right ventricle segmentation in short-axis MRI. In: 2016 Computing in Cardiology Conference (CinC), pp. 485–488. IEEE Computer Society (2016)
Tran, P.V.: A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494 (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), pp. 3431–3440. IEEE Computer Society (2015)
Litjens, G., Kooi, T., Ehteshami, B., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
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
Zotti, C., Luo, Z., Lalande, A., et al.: Novel deep convolution neural network applied to MRI cardiac segmentation. arXiv preprint arXiv:1705.08943 (2017)
Petitjean, C., Zuluaga, M.A., Bai, W., et al.: Right ventricle segmentation from cardiac MRI: a collation study. Med. Image Anal. 19(1), 187–202 (2015)
Chen, L.-C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Yu, F., Koltun V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)
ACDC-MICCAI challenge. http://acdc.creatis.insa-lyon.fr. Accessed 10 July 2018
Keras: The Python Deep Learning library. https://keras.io. Accessed 10 July 2018
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Acknowledgments
The work was supported by the Grant of President of Russian Federation for young scientists No. MK-1896.2017.9 (contract No. 14.W01.17.1896-MK).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Borodin, G., Senyukova, O. (2018). Right Ventricle Segmentation in Cardiac MR Images Using U-Net with Partly Dilated Convolution. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-01421-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01420-9
Online ISBN: 978-3-030-01421-6
eBook Packages: Computer ScienceComputer Science (R0)