Abstract
In this paper, we propose to use deep convolutional neural networks to solve the challenging Intervertebral Disc (IVD) segmentation problem. We investigated the influence of four different patch sampling strategies on the performance of the deep convolutional neural networks. Evaluated on the MICCAI 2015 IVD segmentation challenge datasets, our method achieved a mean Dice overlap coefficient of 89.2% and a mean average absolute surface distance of 1.3 mm. The results achieved by our method are comparable with those achieved by the state-of-the-art methods.
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Acknowledgements
The paper is partially supported by the National Natural Science Funds of China (No. 61571304 and 81571758), and partially supported by the Swiss National Science Foundation Project No. \(205321-157207/1\). The acquisition of original images was supported by the Grant 14431/02/NL/SH2 from the European Space Agency and grant 50WB0720 from the German Aerospace Center (DLR).
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Ji, X., Zheng, G., Belavy, D., Ni, D. (2016). Automated Intervertebral Disc Segmentation Using Deep Convolutional Neural Networks. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Lecture Notes in Computer Science(), vol 10182. Springer, Cham. https://doi.org/10.1007/978-3-319-55050-3_4
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DOI: https://doi.org/10.1007/978-3-319-55050-3_4
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