Abstract
Robust and accurate identification of intervertebral discs from low resolution, sparse MRI scans is essential for the automated scan planning of the MRI spine scan. This paper presents a graphical model based solution for the detection of both the positions and orientations of intervertebral discs from low resolution, sparse MRI scans. Compared with the existing graphical model based methods, the proposed method does not need a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on 25 low resolution, sparse spine MRI data sets verified its performance.
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.
References
Pekar, V., Bystrov, D., Heese, H.S., Dries, S., Schmidt, S., Grewer, R., den Harder, C.J., Bergmans, R.C., Simonetti, A.W., van Muiswinkel, A.: Automated planning of scan geometries in spine mri scans. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 601–608. Springer, Heidelberg (2007)
Weiss, K.L., Storrs, J.M., Banto, R.B.: Automated spine survey iterative scan technique. Radiology 239, 255–262 (2006)
Peng, Z., Zhong, J., Wee, W., Lee, J.: Automated vertebra detection and segmentation from the whole spine mr images. In: IEEE EMBS 2005, vol. 3, pp. 122–133 (2005)
Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Loternz, C.: Automated model-based vertebra detection, identification, and segmentation in ct images. Medical Image Analysis 13, 471–482 (2009)
Štern, D., Likar, B., Pernuš, F., Vrtovec, T.: Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in ct and mr images of lumbar spine. Physics in Medicine and Biology 55, 247–264 (2010)
Schmidt, S., Kappes, J.H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., Schnörr, C.: Spine detection and labeling using a parts-based graphical model. In: MICCAI 2007, pp. 122–133 (2007)
Corso, J.J., Alomari, R.S., Chaudhary, V.: Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 202–210. Springer, Heidelberg (2008)
Dong, X., Zheng, G.: Automated vertebra identification from X-ray images. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6112, pp. 1–9. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, X., Lu, H., Sakurai, Y., Yamagata, H., Zheng, G., Reyes, M. (2010). Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-15948-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15947-3
Online ISBN: 978-3-642-15948-0
eBook Packages: Computer ScienceComputer Science (R0)