Abstract
We introduce a fully automatic localization and segmentation pipeline for three-dimensional (3D) intervertebral discs (IVDs), consisting of a regression-based prediction of vertebral bodies and IVD positions as well as a 3D geodesic active contour segmentation delineating the IVDs. The approach was evaluated on the data set of the challenge in conjunction with the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI–CSI2015, that consists of 15 magnetic resonance images of the lumbar spine with given ground truth segmentations. Based on a localization accuracy of \(3.9 \pm 1.6\) mm, we achieve segmentation results in terms of the Dice similarity coefficient of \(89.1 \pm 2.9\,\%\) averaged over the whole data set.
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Acknowledgements
This work was supported by the province of Styria under the funding scheme “HTI:Tech_for_Med” (ABT08-22-T-7/2013-13) and by the Austrian Science Fund (FWF): P28078-N33.
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Urschler, M., Hammernik, K., Ebner, T., Štern, D. (2016). Automatic Intervertebral Disc Localization and Segmentation in 3D MR Images Based on Regression Forests and Active Contours. 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_13
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DOI: https://doi.org/10.1007/978-3-319-41827-8_13
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