Abstract
In this paper, we present the results of evaluating our fully automatic intervertebral disc (IVD) localization and segmentation method using the training data and the test data provided by the localization and segmentation challenge organizers of the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015. We introduce a validation framework consisting of four standard evaluation criteria to evaluate the performance of our method for both localization and segmentation tasks. More specifically, for localization we propose to use the mean localization distance (MLD) with standard deviation (SD) as well as the successful detection rate with three ranges of accuracy. For segmentation, we propose to use the Dice overlap coefficients (DOC) and average absolute distance (AAD) between the automatic segmented disc surfaces and the associated ground truth. Using the proposed metrics, we first validate our previously introduced approach by conducting a comprehensive leave-one-out experiment on the IVD challenge training data which consists of 15 three-dimensional T2-weighted turbo spin echo magnetic resonance (MR) images and the associated ground truth. For localization, we respectively achieved a successful detection rate of 61, 92, and \(93\,\%\) when the accuracy range is set to 2.0, 4.0, and 6.0 mm, and a mean localization error of \(1.8\,{\pm }\,0.9\) mm. For segmentation, we obtained a mean DOC of \(88\,\%\) and a mean AAD of 1.4 mm. We further evaluated the performance of our approach on the test-1 dataset consisting of five MR images released at the pre-test stage and the test-2 dataset consisting of another five MR images released at the on-site competition stage. The results were obtained with a blind test where the performance evaluations were conducted by the challenge organizers. For localization on the test-1 dataset we achieved a successful detection rate of 91.4, 100.0, and \(100.0\,\%\) with a MLD\(\,{\pm }\,\)SD of \(1.0\,{\pm }\,0.8\) mm, and for localization on the test-2 dataset we achieved a successful detection rate of 77.1, 100.0, and \(100.0\,\%\) with a MLD\(\,{\pm }\,\)SD of \(1.4\,{\pm }\,0.7\) mm, respectively. For segmentation on the test-1 dataset we obtained a mean DOC of \(90\,\%\) and a mean AAD of 1.2 mm, and for segmentation on the test-2 dataset we obtained a mean DOC of \(92\,\%\) and a mean AAD of 1.3 mm, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schmidt, S., Kappes, J.H., Bergtholdt, M., Pekar, V., Dries, S.P.M., Bystrov, D., Schnörr, C.: Spine detection and labeling using a parts-based graphical model. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 122–133. Springer, Heidelberg (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)
Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012)
Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 262–270. Springer, Heidelberg (2013)
Shi, R., Sun, D., Qiu, Z., Weiss, K.: An efficient method for segmentation of MRI spine images. In: Proceedings of IEEE/ICME International Conference on Complex Medical Engineering - CME 2007, pp. 713–717. IEEE (2007)
Michopoulou, S., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., Todd-Pokropek, A.: Atlas-based segmentation of degenerated lumbar intervertebral siscs from MR images of the spine. IEEE Trans. Biomed. Eng. 56(9), 2225–2231 (2009)
Huang, S., Chu, Y., Lai, S., Novak, C.: Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE Trans. Med. Imaging 28(10), 1595–1605 (2009)
Ben Ayed, I., Punithakumar, K., Garvin, G., Romano, W., Li, S.: Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 221–232. Springer, Heidelberg (2011)
Neubert, A., Fripp, J., Shen, K., Salvado, O., Schwarz, R., Lauer, L., Engstrom, C., Crozier, S.: Automatic 3D segmentation of vertebral bodies and intervertebral discs from MRI. In: Proceedings of International Conference on Digital Imaging Computing: Techniques and Applications - DICTA 2011, pp. 9–24 (2011)
Law, M., Tay, K., Leung, A., Garvin, G., Li, S.: Intervertebral disc segmentation in MR images using anisotropic oriented flux. Med. Image Anal. 17(1), 43–61 (2013)
Chen, C., Belavy, D., Yu, W., Chu, C., Armbrecht, G., Bansmann, M., Felsenberg, D., Zheng, G.: Localization and segmentation of 3D intervertebral discs in MR images by data driven estimation. IEEE Trans. Med. Imaging 34(8), 1719–1729 (2015)
Dice, L.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Acknowledgements
This work was partially supported by the Swiss National Science Foundation with Project No. 205321_157207/1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Chu, C., Yu, W., Li, S., Zheng, G. (2016). Localization and Segmentation of 3D Intervertebral Discs from MR Images via a Learning Based Method: A Validation Framework. In: Vrtovec, T., et al. Computational Methods and Clinical Applications for Spine Imaging. CSI 2015. Lecture Notes in Computer Science(), vol 9402. Springer, Cham. https://doi.org/10.1007/978-3-319-41827-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-41827-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41826-1
Online ISBN: 978-3-319-41827-8
eBook Packages: Computer ScienceComputer Science (R0)