Bone Profiles: Simple, Fast, and Reliable Spine Localization in CT Scans
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Algorithms centered around spinal columns in CT data such as spinal canal detection, disk and vertebra localization and segmentation are known to be computationally intensive and memory demanding. The majority of these algorithms need initialization and try to reduce the search space to a minimum. In this work we introduce bone profiles as a simple means to compute a tight ROI containing the spine and seed points within the spinal canal. Bone profiles rely on the distribution of bone intensity values in axial slices. They are easy to understand, and parameters guiding the ROI and seed point detection are straight forward to derive. The method has been validated with two datasets containing 52 general and 242 spine-focused CT scans. Average runtimes of 1.5 and 0.4 s are reported on a single core. Due to its slice-wise nature, the method can be easily parallelized and fractions of the reported runtimes can be further achieved. Our memory requirements are upper bounded by a single CT slice.
KeywordsSpinal Canal Spinal Column Machine Learning Method Seed Point Spine Coverage
The competence center VRVis with the grant number 843272 is funded by BMVIT, BMWFW, and ZIT—The Technology Agency of the City of Vienna within the scope of COMET—Competence Centers for Excellent Technologies. The program COMET is managed by FFG. Thanks go to our project partner AGFA Healthcare for providing data and valuable input.
- 1.Gilad, I., Nissan, M.: Sagittal evaluation of elemental geometrical dimensions of human vertebrae. J. Anat. 143, 115–120 (1985)Google Scholar
- 2.Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Medical Image Computing and Computer-Assisted Intervention, pp. 262–270. Springer, Berlin (2013)Google Scholar
- 3.Graf, F., Kriegel, H.P., Schubert, M., Strukelj, M., Cavallaro, A.: Fully automatic detection of the vertebrae in 2D CT images. In: SPIE Medical Imaging, Vol. 7962 (2011)Google Scholar
- 7.Peng, Z., Zhong, J., Wee, W., Lee, J.h.: Automated vertebra detection and segmentation from the whole spine MR images. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 2527–2530 (2005)Google Scholar
- 10.Yao, J., O’Connor, S.D., Summers, R.M.: Automated spinal column extraction and partitioning. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 390–393 (2006)Google Scholar