Abstract
This paper presents one of the participating methods to the intervertebral disc segmentation challenge organized in conjunction with the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015. The presented method consist of three steps. In the first step, vertebral bodies are detected and labeled using integral channel features and a graphical parts model. The second step consists of image registration, where a set of image volumes with corresponding intervertebral disc atlases are registered to the target volume using the output from the first step as initialization. In the final step, the registered atlases are combined using label fusion to derive the final segmentation. The pipeline was evaluated using a set of \(15+10\) T2-weighted image volumes provided as training and test data respectively for the segmentation challenge. For the training data, a mean disc centroid distance of 0.86 mm and an average DICE score of 91 % was achieved, and for the test data the corresponding results were 0.90 mm and 90 %.
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References
Oktay, A., Albayrak, N., Akgul, Y.: Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images. Comput. Med. Imaging Graph. 38(7), 613–619 (2014)
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)
Ghosh, S., Chaudhary, V.: Supervised methods for detection and segmentation of tissues in clinical lumbar MRI. Comput. Med. Imaging Graph. 38(7), 639–649 (2014)
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)
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)
Neubert, A., Fripp, J., Engstrom, C., Schwarz, R., Lauer, L., Salvado, O., Crozier, S.: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Phys. Med. Biol. 57(24), 8357–8376 (2012)
Lootus, M., Kadir, T., Zisserman, A.: Vertebrae detection and labelling in lumbar MR images. In: Yao, J., et al. (eds.) CSI 2013. LNCV, vol. 17, pp. 219–230. Springer, Switzerland (2014)
Forsberg, D.: Atlas-based registration for accurate segmentation of thoracic and lumbar vertebrae in CT data. In: Yao, J., et al. (eds.) CSI 2014. LNCVB, vol. 20, pp. 49–59. Springer, Switzerland (2015)
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proceedings of 2009 British Machine Vision Conference, BMVC 2009, pp. 91.1–91.11. BMVA Press (2009)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61(1), 55–79 (2005)
Hemmendorff, M., Andersson, M., Kronander, T., Knutsson, H.: Phase-based multidimensional volume registration. IEEE Trans. Med. Imaging 21(12), 1536–1543 (2002)
Eklund, A., Andersson, M., Knutsson, H.: Phase based volume registration using CUDA. In: Proceedings of 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2010, pp. 658–661. IEEE (2010)
Knutsson, H., Andersson, M.: Morphons: segmentation using elastic canvas and paint on priors. In: Proceedings of 12th IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. 1226–1229. IEEE (2005)
Forsberg, D.: Robust image registration for improved clinical efficiency: using local structure analysis and model-based processing. Ph.D. thesis, Linköping University, Sweden (2013)
Forsberg, D., Eklund, A., Andersson, M., Knutsson, H.: Phase-based non-rigid 3D image registration - from minutes to seconds using CUDA. In: Proceedings of Joint MICCAI Workshop on High Performance and Distributed Computing for Medical Imaging - HP/DCI 2011 (2011)
Neubert, A., Fripp, J., Engstrom, C., Crozier, S.: Segmentation of lumbar intervertebral discs from high-resolution 3D MR images using multi-level statistical shape models. In: Yao, J., et al. (eds.) CSI 2013. LNCVB, vol. 17, pp. 173–183. Springer, Switzerland (2014)
Acknowledgements
The work of D.F. was partially funded by the Swedish Innovation Agency (VINNOVA), grant 2014-01422.
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Wang, C., Forsberg, D. (2016). Segmentation of Intervertebral Discs in 3D MRI Data Using Multi-atlas Based Registration. 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_10
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DOI: https://doi.org/10.1007/978-3-319-41827-8_10
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