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A Bypass-Based U-Net for Medical Image Segmentation

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Intelligence Science and Big Data Engineering. Visual Data Engineering (IScIDE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11935))

Abstract

U-Net has been one of the important deep learning models applied for biomedical image segmentation for a few years. In this paper, inspired by the way how fully convolutional network (FCN) makes dense predictions, we modify U-Net by adding a new bypass for the expansive path. Before combining the contracting path with the upsampled output, we connect with the feature maps from a deeper encoding convolutional layer for the decoding up-convolutional units, and sum up the information learned from both sides. Also, we have implemented this modification to recurrent residual convolutional neural network based on U-Net as well. The experimental results show that the proposed bypass-based U-Net can gain further context information, especially the details from the previous convolutional layer, and outperforms the original U-Net on the DRIVE dataset for retinal vessel segmentation and the ISBI 2018 challenge for skin lesion segmentation.

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References

  1. Arganda-Carreras, I., Turaga, S., Berger, D., et al.: Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. 9, 142 (2015)

    Article  Google Scholar 

  2. Chen, H., Qi, X., Cheng, J., Heng, P.: Deep contextual networks for neuronal structure segmentation. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp. 1167–1173. AAAI Press (2016)

    Google Scholar 

  3. Havaei, M., Davy, A., Warde-Farely, D., et al.: Brain tumors segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  4. Yu, L., Yang, X., Chen, H., Qin, J., Heng, P.: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, California, USA, pp. 66–72. AAAI Press (2017)

    Google Scholar 

  5. Zheng, Y., Jiang, Z., Zhang, H., Xie, F., et al.: Histopathological whole slide image analysis using context-based CBIR. IEEE Trans. Med. Imaging 37, 1641–1652 (2017)

    Article  Google Scholar 

  6. Saker, S.: Diabetic retinopathy: in vitro and clinical studies and mechanisms and pharmacological treatments. University of Nottingham (2016)

    Google Scholar 

  7. Jernal, A., Siegel, R., Ward, E., Hao, Y., Xu, J., Thun, M.: Cancer statistics. CA Cancer J. Clin. 59(4), 225–249 (2009)

    Article  Google Scholar 

  8. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: NIP’S Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, LNCS, vol. 1, pp. 1097–1105 (2012)

    Google Scholar 

  9. Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1–9. IEEE (2015)

    Google Scholar 

  10. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Computer Science (2014)

    Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770–778 (2016)

    Google Scholar 

  12. Long, J., Shelhamer, E., Darrel, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 3431–3440. IEEE (2015)

    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 

  14. Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. In: 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands (2018)

    Google Scholar 

  15. Alom, M., Hasan, M., Yakopcic, C., Taha, T., Asari, V.: Recurrent residual convolutional neural network based on u-net (R2U-net) for medical image segmentation (2018). https://arxiv.org/abs/1802.06955

  16. Web page of the digital retinal images for vessel extraction. http://www.isi.uu.nl/Research/Databases/DRIVE/. Accessed 11 Mar 2019

  17. Web page of the ISBI 2018: Skin lesion analysis towards melanoma detection. https://challenge.kitware.com/#challenge/5aab46f156357d5e82b00fe5. Accessed 20 Apr 2019

  18. Codella, N., Rotemberg, V., Tschandl, P., Emre Celebi, M., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC) (2018). https://arxiv.org/abs/1902.03368

  19. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)

    Article  Google Scholar 

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grant 61976229, 61906046, 61572536, 11631015, U1611265 and in part by the Science and Technology Program of Guangzhou under Grant 201804010248.

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Correspondence to Chuan-Xian Ren .

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Chen, K., Xu, G., Qian, J., Ren, CX. (2019). A Bypass-Based U-Net for Medical Image Segmentation. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-36189-1_13

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  • Print ISBN: 978-3-030-36188-4

  • Online ISBN: 978-3-030-36189-1

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