Abstract
Normal Pressure Hydrocephalus (NPH) is a brain disorder that can present with ventriculomegaly and dementia-like symptoms, which often can be reversed through surgery. Having accurate segmentation of the ventricular system into its sub-compartments from magnetic resonance images (MRI) would be beneficial to better characterize the condition of NPH patients. Previous segmentation algorithms need long processing time and often fail to accurately segment severely enlarged ventricles in NPH patients. Recently, deep convolutional neural network (CNN) methods have been reported to have fast and accurate performance on medical image segmentation tasks. In this paper, we present a 3D U-net CNN-based network to segment the ventricular system in MRI. We trained three networks on different data sets and compared their performances. The networks trained on healthy controls (HC) failed in patients with NPH pathology, even in patients with normal appearing ventricles. The network trained on images from HC and NPH patients provided superior performance against state-of-the-art methods when evaluated on images from both data sets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adams, R., Fisher, C., Hakim, S., Ojemann, R., Sweet, W.: Symptomatic occult hydrocephalus with normal cerebrospinal-fluid pressure: a treatable syndrome. N. Engl. J. Med. 273(3), 117–126 (1965)
de Brebisson, A., Montana, G.: Deep neural networks for anatomical brain segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–28 (2015)
Carass, A., et al.: Whole brain parcellation with pathology: validation on ventriculomegaly patients. In: Wu, G. (ed.) Patch-MI 2017. LNCS, vol. 10530, pp. 20–28. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67434-6_3
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Ellingsen, L.M., Roy, S., Carass, A., Blitz, A.M., Pham, D.L., Prince, J.L.: Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling. In: Proceedings of SPIE–the International Society for Optical Engineering, vol. 9784 (2016)
Fischl, B.: Freesurfer. NeuroImage 62(2), 774–781 (2012)
Fonov, V.S., Evans, A.C., McKinstry, R.C., Almli, C., Collins, D.: Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47, S102 (2009)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. arXiv preprint arXiv:1603.05027 (2016)
Hebb, A.O., Cusimano, M.D.: Idiopathic normal pressure hydrocephalus: a systematic review of diagnosis and outcome. Neurosurgery 49(5), 1166–1186 (2001)
Ishikawa, M., et al.: Guidelines for management of idiopathic normal pressure hydrocephalus. Neurol. Med.-Chir. 48(Suppl.), S1–S23 (2008)
Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Ledig, C., et al.: Robust whole-brain segmentation: application to traumatic brain injury. Med. Image Anal. 21(1), 40–58 (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
Roy, S., Butman, J.A., Pham, D.L.: Alzheimers disease neuroimaging initiative, others: robust skull stripping using multiple MR image contrasts insensitive to pathology. NeuroImage 146, 132–147 (2017)
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imag. 29(6), 1310–1320 (2010)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
Wang, H., Suh, J.W., Das, S.R., Pluta, J.B., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Patt. Anal. Mach. Intell. 35(3), 611–623 (2013)
Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80–83 (1945)
Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)
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
Shao, M. et al. (2018). Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks. In: Stoyanov, D., et al. Understanding and Interpreting Machine Learning in Medical Image Computing Applications. MLCN DLF IMIMIC 2018 2018 2018. Lecture Notes in Computer Science(), vol 11038. Springer, Cham. https://doi.org/10.1007/978-3-030-02628-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-02628-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02627-1
Online ISBN: 978-3-030-02628-8
eBook Packages: Computer ScienceComputer Science (R0)