Abstract
Liver disease, especially Non-Alcoholic Fatty Liver Disease has reached high levels, and there is a need for non-invasive tests based on quantitative MRI to replace biopsy in order to better assess liver health. An automated quantitative liver segmentation approach is required to automate these tests and in this work we propose a fully convolutional framework with a novel objective function for quantitative liver segmentation. The method has (to date) been tested on quantitative T1 maps generated from the UK Biobank study. We obtained extremely encouraging results on an unseen test set with a Dice score of 0.95, and Sensitivity 0.98 and Specificity 0.99.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Banerjee, R., Pavlides, M., Tunnicliffe, E.M., Piechnik, S.K., Sarania, N., Philips, R., Collier, J.D., Booth, J.C., Schneider, J.E., Wang, L.M., Delaney, D.W., Fleming, K.A., Robson, M.D., Barnes, E., Neubauer, S.: Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J. Hepatol. 60(1), 69–77 (2014)
Blachier, M., Leleu, H., Peck-Radosavljevic, M., Valla, D.C., Roudot-Thoraval, F.: The burden of liver disease in europe: a review of available epidemiological data. J. Hepatol. 58(3), 593–608 (2013)
Castera, L., Pinzani, M.: Non-invasive assessment of liver fibrosis: are we ready? Lancet 375(9724), 1419 (2010)
Cheng, K., Gu, L., Wu, J., Li, W., Xu, J.: A novel level set based shape prior method for liver segmentation from MRI images. In: Dohi, T., Sakuma, I., Liao, H. (eds.) MIAR 2008. LNCS, vol. 5128, pp. 150–159. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79982-5_17
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Heimann, T., van Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P.M.M., Chi, Y., Cordova, A., Dawant, B.M., Fidrich, M., Furst, J.D., Furukawa, D., Grenacher, L., Hornegger, J., Kainmüller, D., Kitney, R.I., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H.P., Nemeth, G., Raicu, D.S., Rau, A.M., van Rikxoort, E.M., Rousson, M., Rusko, L., Saddi, K.A., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J.M., Wimmer, A., Wolf, I.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
Masoumi, H., Behrad, A., Pourmina, M.A., Roosta, A.: Automatic liver segmentation in mri images using an iterative watershed algorithm and artificial neural network. Biomed. Signal Process. Control 7(5), 429–437 (2012)
Pavlides, M., Banerjee, R., Sellwood, J., Kelly, C.J., Robson, M.D., Booth, J.C., Collier, J., Neubauer, S., Barnes, E.: Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease. J. Hepatol. 64(2), 308–315 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015). http://arxiv.org/abs/1505.04597
Wang, F.S., Fan, J.G., Zhang, Z., Gao, B., Wang, H.Y.: The global burden of liver disease: the major impact of China. Hepatology 60(6), 2099–2108 (2014)
Wilman, H.R., Kelly, M., Garratt, S., Matthews, P.M., Milanesi, M., Herlihy, A., Gyngell, M., Neubauer, S., Bell, J.D., Banerjee, R., et al.: Characterisation of liver fat in the UK Biobank cohort. PLoS One 12(2), e0172921 (2017)
Acknowledgements
This research has been conducted using the UK Biobank Resource under application 9914.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Irving, B. et al. (2017). Deep Quantitative Liver Segmentation and Vessel Exclusion to Assist in Liver Assessment. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-60964-5_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60963-8
Online ISBN: 978-3-319-60964-5
eBook Packages: Computer ScienceComputer Science (R0)