Abstract
Anatomical shape variations are typically difficult to model and parametric or hand-crafted models can lead to ill-fitting segmentations. This difficulty can be addressed with a framework like auto-context, that learns to jointly detect and regularize a segmentation. However, mis-segmentation can still occur when a desired structure, such as the spinal cord, has few locally distinct features. High-level knowledge at a global scale (e.g. an MRI contains a single connected spinal cord) is needed to regularize these candidate segmentations. To encode high-level knowledge, we propose to augment the auto-context framework with global geometric features extracted from the detected candidate shapes. Our classifier then learns these high-level rules and rejects falsely detected shapes. To validate our method we segment the spinal cords from 20 MRI volumes composed of patients with and without multiple sclerosis and demonstrate improvements in accuracy, speed, and manual effort required when compared to state-of-the-art methods.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, M., Carass, A., Cuzzocreo, J., Bazin, P.L., Reich, D.S., Prince, J.L.: Topology preserving automatic segmentation of the spinal cord in magnetic resonance images. In: IEEE ISBI, pp. 1737–1740 (2011)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends® in Computer Graphics and Vision 7(2-3), 81–227 (2011)
Horsfield, M.A., Sala, S., Neema, M., Absinta, M., Bakshi, A., Sormani, M.P., Rocca, M.A., Bakshi, R., Filippi, M.: Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: Application in multiple sclerosis. Neuroimage 50(2), 446–455 (2010)
Kawahara, J., McIntosh, C., Tam, R., Hamarneh, G.: Globally optimal spinal cord segmentation using a minimal path in high dimensions. In: IEEE ISBI, pp. 836–839 (2013)
Kontschieder, P., Kohli, P., Shotton, J., Criminisi, A.: GeoF: Geodesic forests for learning coupled predictors. In: IEEE CVPR (2013)
McIntosh, C., Hamarneh, G.: Spinal crawlers: Deformable organisms for spinal cord segmentation and analysis. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 808–815. Springer, Heidelberg (2006)
McIntosh, C., Hamarneh, G., Toom, M., Tam, R.: Spinal cord segmentation for volume estimation in healthy and multiple sclerosis subjects using crawlers and minimal paths. In: IEEE HISB, pp. 25–31 (2011)
Rocca, M., Horsfield, M., Sala, S., Copetti, M., Valsasina, P., Mesaros, S., Martinelli, V., Caputo, D., Stosic-Opincal, T., Drulovic, J., Comi, G., Filippi, M.: A multicenter assessment of cervical cord atrophy among MS clinical phenotypes. Neurology 76(24), 2096–2102 (2011)
Szummer, M., Kohli, P., Hoiem, D.: Learning CRFs using graph cuts. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 582–595. Springer, Heidelberg (2008)
Tench, C.R., Morgan, P.S., Constantinescu, C.S.: Measurement of cervical spinal cord cross-sectional area by MRI using edge detection and partial volume correction. J. Magn. Reson. Imaging 21(3), 197–203 (2005)
Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE TPAMI 32(10), 1744–1757 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Kawahara, J., McIntosh, C., Tam, R., Hamarneh, G. (2013). Augmenting Auto-context with Global Geometric Features for Spinal Cord Segmentation. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds) Machine Learning in Medical Imaging. MLMI 2013. Lecture Notes in Computer Science, vol 8184. Springer, Cham. https://doi.org/10.1007/978-3-319-02267-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-02267-3_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02266-6
Online ISBN: 978-3-319-02267-3
eBook Packages: Computer ScienceComputer Science (R0)