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Attention deep residual networks for MR image analysis

  • S.I. : Deep Social Computing
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Abstract

Prostate diseases often occur in men. For further clinical treatment and diagnosis, we need to do accurate segmentation on prostate. There are already many methods that concentrate on solving the problem of automatic prostate MR image segmentation. However, the design of some hyperparameters of these methods is migrated from the models that are used for nature images which do not consider the difference between medical image and nature image. Besides, there is trend that researchers are likely to use deeper and more complicated networks to achieve high accuracy. The improvement is limited with surging parameters, computations, training time, and inference time. In this paper, we propose an efficient attention residual U-Net to segment the prostate MR image. We analyze the property of prostate MR image and fine-tune the architecture of U-Net. To accelerate the convergence of our method, residual connection and channel attention are added to our network. A set of experiments suggest our method can achieve a similar accuracy of state of the art with less parameters, less computations, shorter training time, and shorter inference time.

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References

  1. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  2. Huang G, Liu Z, VanDerMaaten L et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  3. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–3440

  4. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  5. Yu L, Yang X, Chen H, et al (2017) Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images. In: Thirty-first AAAI conference on artificial intelligence

  6. Chen H, Dou Q, Yu L et al (2018) VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage 170:446–455

    Article  Google Scholar 

  7. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 234–241

  8. Isensee F, Petersen J, Kohl SAA et al (2019) nnU-Net: breaking the spell on successful medical image segmentation. arXiv preprint arXiv:1904.08128

  9. Yang D, Xu D, Zhou SK et al (2017) Automatic liver segmentation using an adversarial image-to-image network. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 507–515

  10. Sharma N, Aggarwal LM (2010) Automated medical image segmentation techniques. J Med Phys Assoc Med Phys India 35(1):3

    Google Scholar 

  11. Yuan J, Qiu W, Ukwatta E et al (2012) An efficient convex optimization approach to 3D prostate MRI segmentation with generic star shape prior. Prostate MR Image Segment Chall MICCAI 7512:82–89

    Google Scholar 

  12. Birkbeck N, Zhang J, Requardt M, et al (2012) Region-specific hierarchical segmentation of MR prostate using discriminative learning. In: MICCAI grand challenge: prostate MR image segmentation

  13. Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV), IEEE, pp 565–571

  14. Litjens G, Toth R, van de Ven W et al (2014) Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal 18(2):359–373

    Article  Google Scholar 

  15. Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  16. Wang X, Girshick R, Gupta A et al (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803

  17. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  18. Howard AG, Zhu M, Chen B et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  19. Zhang X, Zhou X, Lin M et al (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848–6856

  20. Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel ‘squeeze and excitation’ in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 421–429

  21. Oktay O, Schlemper J, Folgoc LL et al (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999

  22. Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  23. Isola P, Zhu J Y, Zhou T et al (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125–1134

  24. Zhu JY, Park T, Isola P et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

  25. Dou Q, Ouyang C, Chen C et al (2019) PnP-AdaNet: plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7:99065–99076

    Article  Google Scholar 

  26. Liu Y, Chen K, Liu C et al (2019) Structured knowledge distillation for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2604–2613

  27. Drozdzal M, Vorontsov E, Chartrand G et al (2016) The importance of skip connections in biomedical image segmentation. In: Deep learning and data labeling for medical applications, Springer, Cham, pp 179–187

  28. Han D, Kim J, Kim J (2017) Deep pyramidal residual networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5927–5935

  29. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167

  30. Kervadec H, Bouchtiba J, Desrosiers C et al (2019) Boundary loss for highly unbalanced segmentation. In: International conference on medical imaging with deep learning, pp 285–296

  31. https://github.com/Lyken17/pytorch-OpCounter

  32. Kingma DP, Ba JA (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  33. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

  34. Xue Y, Xu T, Zhang H et al (2018) SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16:383–392

    Article  Google Scholar 

Download references

Funding

This work is supported by the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant No. 2019AEA170.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Bayi Road, Wuhan, Hubei, China

    Mengqing Mei & Fazhi He

  2. Department of Computing, Macquarie University, Sydney, Australia

    Shan Xue

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  1. Mengqing Mei
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  2. Fazhi He
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  3. Shan Xue
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Correspondence to Fazhi He.

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Mei, M., He, F. & Xue, S. Attention deep residual networks for MR image analysis. Neural Comput & Applic 35, 12957–12966 (2023). https://doi.org/10.1007/s00521-020-05083-3

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