Lecture Notes in Computational Vision and Biomechanics

Vertebrae Segmentation in 3D CT Images Based on a Variational Framework. Hammernik, K., Ebner, T., Stern, D., Urschler, M., & Pock, T. Volume 20, Yao, J., Glocker, B., Klinder, T., & Li, S., editors. Lecture Notes in Computational Vision and Biomechanics, pages 227-233. Springer, Cham, 2015.

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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 ± 0.04 averaged over the whole data set.

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 author = {Hammernik, Kerstin and Ebner, Thomas and Stern, Darko and Urschler, Martin and Pock, Thomas},
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 chapter = {Vertebrae Segmentation in 3D CT Images Based on a Variational Framework},
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