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3D Patchwise U-Net with Transition Layers for MR Brain Segmentation

  • Miguel Luna
  • Sang Hyun ParkEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

We propose a new patch based 3D convolutional neural network to automatically segment multiple brain structures on Magnetic Resonance (MR) images. The proposed network consists of encoding layers to extract informative features and decoding layers to reconstruct the segmentation labels. Unlike the conventional U-net model, we use transition layers between the encoding layers and the decoding layers to emphasize the impact of feature maps in the decoding layers. Moreover, we use batch normalization on every convolution layer to make a well generalized model. Finally, we utilize a new loss function which can normalize the categorical cross entropy to accurately segment the relatively small interest regions which are opt to be misclassified. The proposed method ranked 1\(^{st}\) over 22 participants at the MRBrainS18 segmentation challenge at MICCAI 2018.

Keywords

Convolutional neural network Brain MR image Semantic segmentation Transition layer Normalized cross entropy 

Notes

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2018R1D1A1B07044473).

References

  1. 1.
    Akkus, Z., Galimzianova, A., Hoogi, A.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30, 449 (2017)CrossRefGoogle Scholar
  2. 2.
    Chen, H., Dou, Q., Yu, L., Heng, P.A.: VoxResNet: deep voxelwise residual networks for volumetric brain segmentation. arXiv:1608.05895 (2016)
  3. 3.
    Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611 (2018)CrossRefGoogle Scholar
  4. 4.
    Cui, S., Mao, L., Jiang, J., Liu, C., Xiong, S.: Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J. Healthcare Eng. 2018, 14 (2018)Google Scholar
  5. 5.
    Despotovic, I., Goossens, B., Philips, W.: MRI segmentation of the human brain: challenges, methods, and applications. Comput. Math. Methods Med. 2015, 23 (2015). Article ID 450341CrossRefGoogle Scholar
  6. 6.
    Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., Ayed, I.B.: HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. arXiv:1804.02967 (2018)
  7. 7.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, pp. 448–456 (2015)Google Scholar
  8. 8.
    Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2016)CrossRefGoogle Scholar
  9. 9.
    Kuijf, H.J., Bennink, E.: Grand challenge on MR brain segmentation at MICCAI 2018. http://mrbrains18.isi.uu.nl
  10. 10.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)Google Scholar
  11. 11.
    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_28CrossRefGoogle Scholar
  12. 12.
    Shreyas, V., Pankajakshan, V.: A deep learning architecture for brain tumor segmentation in MRI images. In: 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP) (2017)Google Scholar
  13. 13.
    Wu, Z., Shen, C., Hengel, A.: High-performance semantic segmentation using very deep fully convolutional networks. arXiv:1604.04339 (2016)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Robotics EngineeringDGISTDaeguSouth Korea

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