Advertisement

Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation

  • Dong Yang
  • Holger Roth
  • Ziyue Xu
  • Fausto Milletari
  • Ling Zhang
  • Daguang XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11765)

Abstract

Deep neural network (DNN) based approaches have been widely investigated and deployed in medical image analysis. For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation. Even the baseline neural network models (U-Net, V-Net, etc.) have been proven to be very effective and efficient when the training process is set up properly. Nevertheless, to fully exploit the potentials of neural networks, we propose an automated searching approach for the optimal training strategy with reinforcement learning. The proposed approach can be utilized for tuning hyper-parameters, and selecting necessary data augmentation with certain probabilities. The proposed approach is validated on several tasks of 3D medical image segmentation. The performance of the baseline model is boosted after searching, and it can achieve comparable accuracy to other manually-tuned state-of-the-art segmentation approaches.

References

  1. 1.
    Medical decathlon challenge (2018). http://medicaldecathlon.com
  2. 2.
    Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)Google Scholar
  3. 3.
    Choy, G., et al.: Current applications and future impact of machine learning in radiology. Radiology 288(2), 318–328 (2018).  https://doi.org/10.1148/radiol.2018171820CrossRefGoogle Scholar
  4. 4.
    Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016).  https://doi.org/10.1109/cvpr.2016.90
  6. 6.
    Isensee, F., et al.: nnU-Net: self-adapting framework for U-Net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)
  7. 7.
    Liu, C., et al.: Auto-DeepLab: hierarchical neural architecture search for semantic image segmentation. arXiv preprint arXiv:1901.02985 (2019)
  8. 8.
    Liu, C., et al.: Progressive neural architecture search. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 19–35. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01246-5_2CrossRefGoogle Scholar
  9. 9.
    Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)
  10. 10.
    Liu, S., et al.: 3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 851–858. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00934-2_94CrossRefGoogle Scholar
  11. 11.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016).  https://doi.org/10.1109/3dv.2016.79
  12. 12.
    Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameter sharing. In: International Conference on Machine Learning, pp. 4092–4101 (2018)Google Scholar
  13. 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_28CrossRefGoogle Scholar
  14. 14.
    Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
  15. 15.
    Xu, H., Zhang, H., Hu, Z., Liang, X., Salakhutdinov, R., Xing, E.: AutoLoss: learning discrete schedules for alternate optimization. arXiv preprint arXiv:1810.02442 (2018)
  16. 16.
    Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)
  17. 17.
    Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018).  https://doi.org/10.1109/cvpr.2018.00907

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dong Yang
    • 1
  • Holger Roth
    • 1
  • Ziyue Xu
    • 1
  • Fausto Milletari
    • 1
  • Ling Zhang
    • 1
  • Daguang Xu
    • 1
    Email author
  1. 1.NVIDIABethesdaUSA

Personalised recommendations