Abstract
The vertebral column is of particular importance for many clinical procedures such as anesthesia or anaelgesia. One of the main challenges for diagnostic and interventional tasks at the spine is its robust and accurate segmentation. There exist a number of segmentation approaches that mostly perform segmentation on the individual vertebrae. We present a novel segmentation approach that uses statistical multi-object shape\(+\)pose models and evaluate it on a standardized data set. We could achieve a mean dice coefficient of \(0.83\) for the segmentation. The flexibility of our approach let it become valuable for the specific segmentation challenges in clinical routine.
A. Seitel and A. Rasoulian contributed equally to this work.
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This work was funded by the CIHR.
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Seitel, A., Rasoulian, A., Rohling, R., Abolmaesumi, P. (2015). Lumbar and Thoracic Spine Segmentation Using a Statistical Multi-object Shape\(+\)Pose Model. 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_19
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DOI: https://doi.org/10.1007/978-3-319-14148-0_19
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