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Segmentation of Brachial Plexus Ultrasound Images Based on Modified SegNet Model

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Intelligent Data Engineering and Automated Learning – IDEAL 2023 (IDEAL 2023)

Abstract

The images automatic segmentation is an important technique for medical treatment. It can help doctor to relieve from heavy works of reading ultrasound images, especially for brachial plexus images. Moreover, the deep learning technology assists doctors in locating the catheters and improves the efficiency and accuracy of injection. However, the research of brachial plexus ultrasonic image segmentation is too few to satisfy the needs of medical application. In this paper, we used a novel modified SegNet to accurately segment brachial plexus. In the training stage, the original training set was divided into two parts randomly (training set 90% and validation set 10%), and the parameters of models were determined and optimized by adopting cross-validation method and data augmentation which can avoid over-fitting effectively. Computational results show that, the model significantly increases nerve segmentation accuracy with 96%; meanwhile, the model is scored 0.644 by Kaggle competition (The Kaggle competition uses CSV files containing the final results for scoring. Besides, the Kaggle competition does not require participants to provide open source code, and all participants’ competition scores and rankings can be found on the website: https://www.kaggle.com/c/ultrasound-nerve-segmentation/leaderboard).

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Notes

  1. 1.

    https://github.com/alexgkendall/SegNet-Tutorial.

  2. 2.

    We did not change its decoding format, but modified its up-sampling index number in our model because of the different sizes with the original types.

  3. 3.

    This image is a clip of the original dynamic images that we downloaded from: https://www.kaggle.com/chefele/ultrasound-nerve-segmentation/animated-images-with-outlined-nerve-area/code.(the red thin bounding line is the post-production mark.).

References

  1. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, vol. 25, no. 2, pp. 1097–1105 (2012)

    Google Scholar 

  2. Szegedy, C., Liu, W., Jia, Y.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 22, no. 4, pp. 1889–1897 (2016)

    Google Scholar 

  3. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (ICLR), pp. 1–14 (2015)

    Google Scholar 

  4. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 79, no. 10, pp. 3431–3440. IEEE Computer Society, Washington DC (2015)

    Google Scholar 

  5. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2015)

    Article  Google Scholar 

  6. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 11, pp. 1520–1528 (2016)

    Google Scholar 

  7. Liang-Chieh, C., George, P., Iasonas, K., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Google Scholar 

  8. Gao, X., Lin, S., Wong, T.Y.: Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans. Biomed. Eng. 62(11), 2693–2701 (2015)

    Article  Google Scholar 

  9. Yang, Z., Zhong, S., Carass, A., Ying, S.H., Prince, J.L.: Deep learning for cerebellar ataxia classification and functional score regression. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 68–76. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10581-9_9

    Chapter  Google Scholar 

  10. Lee, H., Chen, Y.P.: Image based computer aided diagnosis system for cancer detection. Expert Syst. Appl. 42(12), 5356–5365 (2015)

    Article  Google Scholar 

  11. Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. arXiv:1511.02680v1 (2015). https://arxiv.org/abs/1511.02680

  12. Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM, New York (2014)

    Google Scholar 

  13. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

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Correspondence to Xiujiao Chen .

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Yan, S., Chen, X., Tang, X., Chi, X. (2023). Segmentation of Brachial Plexus Ultrasound Images Based on Modified SegNet Model. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_31

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  • DOI: https://doi.org/10.1007/978-3-031-48232-8_31

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