Abstract
In this paper, we present a method for automatic segmentation of lumbar vertebrae from a given lumbar spinal CT image. More specifically, our automatic lumbar vertebrae segmentation method consists of two steps: affine atlas-target registration-based label fusion and bone-sheetness assisted multi-label grid cut which has the inherent advantage of automatic separation of the five lumbar vertebrae from each other. We evaluate our method on 21 clinical lumbar spinal CT images with the associated manual segmentation and conduct a leave-one-out study. Our method achieved an average Dice coefficient of 93.9 ± 1.0% and an average symmetric surface distance of 0.41 ± 0.08 mm.
W. YU and W. Liu mdash Contributed equally to this paper.
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References
Yao, J., Burns, J., Forsberg, D., et al.: A multi-center milestone study of clinical vertebral CT segmentation. Comput. Med. Imaging Graph. 49, 16–28 (2016)
Chu, C., Belavy, D., et al.: Fully automatic localization and segmentation of 3d vertebral bodies from CT/MR images via a learning-based method. PLoS One 10(11), e0143327 (2015)
Sekuboyina, A., Kukacka, J., Kirschke, J., Menze, B., Valentinitsch, A.: Attention-driven deep learning for pathological spine segmentation. In: Proceedings of the 2017 MICCAI-MSKI Workshop, pp. 104–115 (2017)
Damopoulos, D., Glocker, B., Zheng, G.: Automatic localization of the lumbar vertebral landmarks in CT images with context features. In: Proceedings of the 2017 MICCAI-MSKI Workshop, pp. 56–68 (2017)
Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)
Jamriška, O., Sýkora, D., Hornung, A.: Cache-efficient graph cuts on structured grids. In: Proceedings of the CVPR 2012, pp. 3673–3680 (2012)
Descoteaux, M., Audette, M., Chinzei, K., Siddiqi, K.: Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery. In: Proceedings of the MICCAI 2005, pp. 9–16 (2005)
Acknowledgments
The work is supported by the Swiss National Science Foundation (Grant no. 205321_157207) and Beijing Natural Science Foundation (Grant no. Z170001).
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Yu, W., Liu, W., Tan, L., Zhang, S., Zheng, G. (2018). Affinely Registered Multi-object Atlases as Shape Prior for Grid Cut Segmentation of Lumbar Vertebrae from CT Images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_11
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DOI: https://doi.org/10.1007/978-3-319-93000-8_11
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