Abstract
Automatic segmentation of 3D vertebrae is a challenging task in medical imaging. In this paper, we introduce a total variation (TV) based framework that incorporates an a priori model, i.e., a vertebral mean shape, image intensity and edge information. The algorithm was evaluated using leave-one-out cross validation on a data set containing ten computed tomography scans and ground truth segmentations provided for the CSI MICCAI 2014 spine and vertebrae segmentation challenge. We achieve promising results in terms of the Dice Similarity Coefficient (DSC) of \(0.93 \pm 0.04\) averaged over the whole data set.
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Acknowledgments
This work was supported by the Austrian Science Fund (FWF) under the START project BIVISION, No. Y729, by the city of Graz (A16-21628/2013), and a European Community FP7 Marie Curie Intra European Fellowship (331239).
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Hammernik, K., Ebner, T., Stern, D., Urschler, M., Pock, T. (2015). Vertebrae Segmentation in 3D CT Images Based on a Variational Framework. In: Yao, J., Glocker, B., Klinder, T., Li, S. (eds) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-14148-0_20
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DOI: https://doi.org/10.1007/978-3-319-14148-0_20
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