Abstract
Segmentation of grey matter in magnetic resonance images of the spinal cord is an important step in assessing disease state in neurological disorders such as multiple sclerosis. However, manual delineation of spinal cord tissue is time-consuming and susceptible to variability introduced by the rater. We present a novel segmentation method for spinal cord tissue that uses fully convolutional encoder networks (CENs) for direct end-to-end training and includes shortcut connections to combine multi-scale features, similar to a u-net. While CENs with shortcuts have been used successfully for brain tissue segmentation, spinal cord images have very different features, and therefore deserve their own investigation. In particular, we develop the methodology by evaluating the impact of the number of layers, filter sizes, and shortcuts on segmentation accuracy in standard-resolution cord MRIs. This deep learning-based method is trained on data from a recent public challenge, consisting of 40 MRIs from 4 unique scan sites, with each MRI having 4 manual segmentations from 4 expert raters, resulting in a total of 160 image-label pairs. Performance of the method is evaluated using an independent test set of 40 scans and compared against the challenge results. Using a comprehensive suite of performance metrics, including the Dice similarity coefficient (DSC) and Jaccard index, we found shortcuts to have the strongest impact (0.60 to 0.80 in DSC), while filter size (0.76 to 0.80) and the number of layers (0.77 to 0.80) are also important considerations. Overall, the method is highly competitive with other state-of-the-art methods.
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
Bakshi, R., et al.: Measurement of brain and spinal cord atrophy by magnetic resonance imaging as a tool to monitor multiple sclerosis. J. Neuroimag. 15(3) (2004). Offcial journal of the American Society of Neuroimaging
Brosch, T., et al.: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5), 1229–1239 (2016)
Brosch, T., et al.: Convolutional encoder networks for multiple sclerosis lesion segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, pp. 3–11 (2015). ISBN 9783319245737
De Leener, B., et al.: SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. NeuroImage 145 (2017)
Drozdzal, M., et al.: The Importance of skip connections in biomedical image segmentation. CoRR abs/1608.04117 (2016)
Lee, H., Ng, A.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (2009)
Lukas, C., et al.: Relevance of spinal cord abnormalities to clinical Disability in Multiple sclerosis: MR Imaging Findings in a Large Cohort of Patients. Radiology 269 (2013)
Prados, F., et al.: Spinal cord grey matter segmentation challenge. NeuroImage 152 (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). doi:10.1007/978-3-319-24574-4_28
Schlaeger, R., et al.: Spinal cord gray matter atrophy correlates with multiple sclerosis disability. Ann. Neurol. 76(4) (2014)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016). ISSN 1558254X
Zeiler, M.: ADADELTA: an adaptive learning rate method. arXiv arXiv:1212.5701 (2012)
Zeiler, M., Taylor, G., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: Proceedings of the IEEE International Conference on Computer Vision (2011). ISSN 1550–5499
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
Porisky, A. et al. (2017). Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-67558-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67557-2
Online ISBN: 978-3-319-67558-9
eBook Packages: Computer ScienceComputer Science (R0)