Skip to main content
Springer Nature Link
Log in

Dense gate network for biomedical image segmentation

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Deep learning has recently shown its outstanding performance in biomedical image semantic segmentation. Most biomedical semantic segmentation frameworks comprise the encoder–decoder architecture directly fusing features of the encoder and the decoder by the way of skip connections. However, the simple fusion operation may neglect the semantic gaps which lie between these features in the decoder and the encoder, hindering the effectiveness of the network.

Methods

Dense gate network (DG-Net) is proposed for biomedical image segmentation. In this model, the Gate Aggregate structure is utilized to reduce the semantic gaps between features in the encoder and the corresponding features in the decoder, and the gate unit is used to reduce the categorical ambiguity as well as to guide the low-level high-resolution features to recover semantic information. Through this method, the features could reach a similar semantic level before fusion, which is helpful for reducing semantic gaps, thereby producing accurate results.

Results

Four medical semantic segmentation experiments, based on CT and microscopy images datasets, were performed to evaluate our model. In the cross-validation experiments, the proposed method achieves IOU scores of 97.953%, 89.569%, 81.870% and 76.486% on these four datasets. Compared with U-Net and MultiResUNet methods, DG-Net yields a higher average score on IOU and Acc.

Conclusion

The DG-Net is competitive with the baseline methods. The experiment results indicate that Gate Aggregate structure and gate unit could improve the performance of the network by aggregating features from different layers and reducing the semantic gaps of features in the encoder and the decoder. This has potential in biomedical image segmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: CVPR. arxiv:1411.4038

  2. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: CVPR. arxiv:1411.4038

  3. Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder–decoder with atrous separable convolution for semantic image segmentation. In: CVPR. arxiv:1802.0ds2611fds

  4. Yu C, Wang J, Peng C, Gao C, Yu G, Sang N (2018) BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: ECCV. arxiv:1808.00897

  5. Chaurasia A, Culurciello E (2017) LinkNet: exploiting encoder representations for efficient semantic segmentation. In: VCIP 2017. arxiv:1707.0378

  6. Pohlen T, Hermans A, Mathias M, Leibe B (2017) Full-resolution residual networks for semantic segmentation in street scenes. In: CVPR. arxiv:1611.08323

  7. Meletis Panagiotis, Dubbelman Gijs (2018) Training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation. IEEE Intell Veh Symp (IV) 2018:1045–1105

    Google Scholar 

  8. Fu H, Xu Y, Wing D, Wong K, Liu J (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE

  9. Dou Q, Chen H, Jin Y, Yu L, Qin J, Heng P-A (2016) 3D Deeply supervised network for automatic liver segmentation from CT volumes. In: CVPR 2016. arxiv:1607.00582

  10. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. arxiv:1505.04597

  11. Beers A, Chang K, Brown J, Sartor E, Mammen CP, Gerstner E, Rosen B, Kalpathy-Cramer J (2017) Sequential 3D U-Nets for biologically-informed brain tumor segmentation. In: CVPR 2017. arxiv:1709.02967

  12. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018). Attention U-Net: learning where to look for the pancreas. In: 1st conference on medical imaging with deep learning (MIDL 2018). arxiv:1804.03999

  13. Kamrul Hasan SM, Linte CA (2019) U-NetPlus: a modified encoder–decoder U-Net architecture for semantic and instance segmentation of surgical instrument. arixv:1902.08994

  14. Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C (2016) The importance of skip connections in biomedical image segmentation. arxiv:1608.04117

  15. Zhang Z, Zhang X, Peng C, Cheng D, Sun J (2018) ExFuse: enhancing feature fusion for semantic segmentation. In: European conference on computer vision. Springer, Cham. arxiv:1804.03821

  16. Ibtehaz N, Sohel Rahman M (2019) MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. arxiv:1902.04049

  17. Yu F, Wang D, Shelhamer E, Darrell T (2018) Deep layer aggregation. In: CVPR 2018. arxiv:1707.06484

  18. Huang G, Liu Z, van der Maaten L (2016) Densely connected convolutional networks. 2016. In: CVPR. arxiv:1608.06993

  19. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) UNet++: a nested U-Net architecture for medical image segmentation. In: LMIA. arxiv:1807.10165

  20. Islam MA, Rochan M, Bruce NDB, Wang Y (2017) Gated feedback refinement network for dense image labeling. In: CVPR

  21. Ulman V, Maška M, Magnusson KEG, Ronneberger O, Haubold C, Harder N, Matula P, Matula P, Svoboda D, Radojevic M, Smal I, Rohr K, Jaldén J, Blau HM, Dzyubachyk O, Lelieveldt B, Xiao P, Li Y, Cho S-Y, Dufour AC, Olivo-Marin J-C, Reyes-Aldasoro CC, Solis-Lemus JA et al (2017) An objective comparison of cell-tracking algorithms. Nat Methods 14:1141–1152

    Article  CAS  Google Scholar 

  22. Maška M, Ulman V, Svoboda D, Matula P, Matula P, Ederra C, Urbiola A, Españ T, Venkatesan S, Balak DMW, Karas P, Bolcková T, Štreitová M, Carthel C, Coraluppi S, Harder N, Rohr K, Magnusson KEG, Jaldén J, Blau HM, Dzyubachyk O, Křížek P, Hagen GM, Escuredo DP, Jimenez-Carretero D, Ledesma-Carbayo MJ, Muñoz-Barrutia A, Meijering E, Kozubek M, Ortiz-de-Solorzano C (2014) A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11):1609–1617

    Article  Google Scholar 

  23. Bloice Marcus D, Roth Peter M, Holzinger A (2019) Biomedical image augmentation using Augmentor. Bioinformatics 35(21):4522–4524. https://doi.org/10.1093/bioinformatics/btz259

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Dr. Jerod Michel for his help in polishing the language.

Funding

This study was funded by the National Natural Science Foundation of China (Grant Nos. 61773205, 61171059).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Dongsheng Li, Chunxiao Chen, Jianfei Li & Liang Wang

Authors
  1. Dongsheng Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chunxiao Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jianfei Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Liang Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chunxiao Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The mouse CT images were scanned in Southeast University, and the experiment was approved by the Animal Ethics Committee of Southeast University. All applicable Animal Ethics Committee of Southeast University guidelines for the care and use of animals were followed.

Informed consent

This article does not contain patient data.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, D., Chen, C., Li, J. et al. Dense gate network for biomedical image segmentation. Int J CARS 15, 1247–1255 (2020). https://doi.org/10.1007/s11548-020-02138-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-020-02138-7

Keywords