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An improved CapsNet applied to recognition of 3D vertebral images

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Abstract

Deep learning is currently widely applied in medical image processing and has achieved good results. However, recognizing vertebrae via image processing remains a challenging problem due to their complex spatial structures. CapsNet is a newly proposed network whose characteristics compensate for some shortcomings of traditional CNNs, and it has been shown to perform well on many tasks, including medical image recognition. In this paper, we applied a modified CapsNet to recognise 3D vertebral images by introducing an RNN module into CapsNet to further enhance its learning ability. This new network is called RNNinCaps, and it achieves the highest recognition performance on 3D vertebral images (the average accuracy of RNNinCaps exceeds the accuracy of the original CapsNet by 46.2% and that of a traditional CNN by 12.6%). RNNinCaps also performs better than several mainstream networks. RNNinCaps can promotes CapsNet’s application in the field of 3D medical image recognition.

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Notes

  1. The source website of this picture is : http://spineweb.digitalimaginggroup.ca/spineweb/uploads/Main/uw_database_overview.png

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 61872407, Grant 61572167 and Grant 61571176.

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  1. Hefei University of Technology, 420 Feicui Road, Hefei, Anhui, China

    Hao Wang, Kun Shao & Xing Huo

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  1. Hao Wang
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  2. Kun Shao
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  3. Xing Huo
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Correspondence to Xing Huo.

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The authors declared that they have no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Wang, H., Shao, K. & Huo, X. An improved CapsNet applied to recognition of 3D vertebral images. Appl Intell 50, 3276–3290 (2020). https://doi.org/10.1007/s10489-020-01695-3

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