SpringerLink
Log in

MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation

  • Research
  • Published:
Health Information Science and Systems Aims and scope Submit manuscript

Abstract

Biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions to biomedical image segmentation applications. In this paper, we propose three different multi-scale dense connections (MDC) for the encoder, the decoder of U-shaped architectures, and across them. Based on three dense connections, we propose a multi-scale densely connected U-Net (MDU-Net) for biomedical image segmentation. MDU-Net directly fuses the neighboring feature maps with different scales from both higher layers and lower layers to strengthen feature propagation in the current layer. Multi-scale dense connections, which contain shorter connections between layers close to the input and output, also make a much deeper U-Net possible. Besides, we introduce quantization to alleviate the potential overfitting in dense connections, and further improve the segmentation performance. We evaluate our proposed model on the MICCAI 2015 Gland Segmentation (GlaS) dataset. The three MDC improve U-Net performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile, the MDU-Net with quantization obviously improves the segmentation performance of original U-Net.

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
EUR 32.99 /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 includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

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

  2. Guan S, Khan A, Sikdar S, Chitnis P.V. Fully dense unet for 2d sparse photoacoustic tomography artifact removal 2018.

  3. Dong L, He L, Mao M, Kong G, Wu X, Zhang Q, Cao X, Izquierdo E. Cunet: a compact unsupervised network for image classification. IEEE Transactions on Multimedia. 2018;20(8):2012–21.

    Google Scholar 

  4. Raza SEA, Cheung L, Epstein D, Pelengaris S, Khan M, Rajpoot N.M. Mimo-net: a multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). 201. p. 337–340.

  5. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging. 2019;39(6):1856–67.

    Article  Google Scholar 

  6. Yin XX, Sun L, Fu Y, Lu R, Zhang Y. U-net-based medical image segmentation. J Healthc Eng. 2022. https://doi.org/10.1155/2022/4189781.

    Article  Google Scholar 

  7. Farabet C, Couprie C, Najman L, LeCun Y. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;35(8):1915–29.

    Article  Google Scholar 

  8. Newell A, Yang K, Deng J. Stacked hourglass networks for human pose estimation. In: European Conference on Computer Vision, Springer 2016. p. 483–499.

  9. Tang Z, Peng X, Geng S, Wu L, Zhang S, Metaxas D. Quantized densely connected u-nets for efficient landmark localization. In: European Conference on Computer Vision (ECCV) 2018.

  10. Yang W, Li S, Ouyang W, Li H, Wang X. Learning feature pyramids for human pose estimation. In: The IEEE International Conference on Computer Vision (ICCV). Volume 2. 2017.

  11. Lin G, Milan A, Shen C, Reid ID. Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In: Cvpr. Volume 1. 2017. p. 5.

  12. Tan W, Liu Y, Liu H, Yang J, Yin X, Zhang Y. A segmentation method of lung parenchyma from chest ct images based on dual u-net. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE 2019. p. 1649–1656.

  13. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 1–9.

  14. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 2818–2826.

  15. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016. p. 770–778.

  16. Huang G, Sun Y, Liu Z, Sedra D. Weinberger. K.Q. Deep networks with stochastic depth. 2016. p. 646–61.

  17. Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 6230–6239.

  18. Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C. The importance of skip connections in biomedical image segmentation. 2016. p. 179–187.

  19. Huang G, Liu Z, Maaten LVD, Weinberger KQ. Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 2261–2269.

  20. Jégou S, Drozdzal M, Vazquez D, Romero A, Bengio Y. The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. 2017. p. 11–19.

  21. Yang M, Yu K, Zhang C, Li Z, Yang K. Denseaspp for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 3684–3692.

  22. Bilinski P, Prisacariu V. Dense decoder shortcut connections for single-pass semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 6596–6605.

  23. Chen LC, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. ar**v preprint ar**v:1706.05587 2017.

  24. ** KH, Mccann MT, Froustey E, Unser M. Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society. 2016;26(9):4509–22.

    Article  MathSciNet  Google Scholar 

  25. Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA. H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Transactions on Medical Imaging. 2017. https://doi.org/10.1109/TMI.2018.2845918.

    Article  Google Scholar 

  26. Chen LC, Yang Y, Wang J, Xu W, Yuille AL. Attention to scale: scale-aware semantic image segmentation. In: Computer Vision and Pattern Recognition. 2016. p. 3640–3649.

  27. Lin G, Shen C, Van Den Hengel A, Reid I. Efficient piecewise training of deep structured models for semantic segmentation. 2016. p. 3194–3203.

  28. Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. CoRR ar**v:1511.07122 2015.

  29. Dai J, Qi H, **ong Y, Li Y, Zhang G, Hu H, Wei Y. Deformable convolutional networks. 2017. p. 764–773.

  30. Zhang J, Zhang Y, Xu X. Pyramid u-net for retinal vessel segmentation. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE 2021. p. 1125–1129.

  31. Zhang J, Zhang Y, Zhu S, Xu X. Constrained multi-scale dense connections for accurate biomedical image segmentation. In: BIBM, IEEE 2020. p. 877–884.

  32. Al-Masni MA, Kim DH. Cmm-net: contextual multi-scale multi-level network for efficient biomedical image segmentation. Sci Rep. 2021;11(1):1–18.

    Article  Google Scholar 

  33. Jacobs JG, Panagiotaki E, Alexander DC. Gleason grading of prostate tumours with max-margin conditional random fields. In: International Workshop on Machine Learning in Medical Imaging, Springer 2014. p. 85–92.

  34. Nguyen K, Sarkar A, Jain AK. Structure and context in prostatic gland segmentation and classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer 2012. p. 115–123.

  35. Sirinukunwattana K, Snead DR, Rajpoot NM. A novel texture descriptor for detection of glandular structures in colon histology images. In: Medical Imaging 2015: Digital Pathology. 9420, International Society for Optics and Photonics 2015. p. 94200S.

  36. Fu H, Qiu G, Shu J, Ilyas M. A novel polar space random field model for the detection of glandular structures. IEEE Transactions on Medical Imaging. 2014;33(3):764–76.

    Article  Google Scholar 

  37. Dhungel N, Carneiro G, Bradley AP. Deep learning and structured prediction for the segmentation of mass in mammograms. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer 2015. p. 605–612.

  38. Dou Q, Chen H, Yu L, Zhao L, Qin J, Wang D, Mok VC, Shi L, Heng PA. Automatic detection of cerebral microbleeds from mr images via 3d convolutional neural networks. IEEE Transactions on Medical Imaging. 2016;35(5):1182–95.

    Article  Google Scholar 

  39. Roth HR, Lu L, Farag A, Shin HC, Liu J, Turkbey EB, Summers RM. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer 2015. p. 556–564.

  40. Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep learning with radiomics for disease diagnosis and treatment: challenges and potential. Front Oncol. 2022. https://doi.org/10.3389/fonc.2022.773840.

    Article  Google Scholar 

  41. **aowei X, Lu Q, Yang L, Hu S, Chen D, Hu Y, Shi Y. Quantization of fully convolutional networks for accurate biomedical image segmentation. Preprint at ar**v:1803.04907 2018.

  42. Wen Z, Liu J, Li Y. Gcsba-net: Gabor-based and cascade squeeze bi-attention network for gland segmentation. IEEE J-BHI 2020.

  43. Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2021;18(2):203–11.

    Article  Google Scholar 

  44. Zhou A, Yao A, Guo Y, Xu L, Chen Y. Incremental network quantization: towards lossless cnns with low-precision weights. 2016.

Download references

Acknowledgements

This work was supported by the Major Key Project of PCL (Grant Nos. PCL2022A03, PCL2021A02, PCL2021A09), the Natural Science Foundation of Guangdong Province (Grant No. 2022A1515010157), the National Key Research and Development Program of China (Grant No. 2018YFC1002600), the Science and Technology Planning Project of Guangdong Province, China (Grant Nos. 2017B090904034, 2017B030314109, 2018B090944002, 2019B020230003), Guangdong Peak Project (Grant No. DFJH201802), and the National Natural Science Foundation of China (Grant No. 62006050).

Author information

Authors and Affiliations

  1. The Department of New Networks, Peng Cheng Laboratory, Shenzhen, Guangdong, China

    Jiawei Zhang & Yanchun Zhang

  2. Department of Cardiovascular Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences),Southern Medical University, Guangzhou, Guangdong, China

    Jiawei Zhang & **aowei Xu

  3. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, Guangdong, China

    Jiawei Zhang & Yanchun Zhang

  4. Institute for Sustainable Industries & Livable Cities, Victoria University, Melbourne, VIC, Australia

    Jiawei Zhang & Yanchun Zhang

  5. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

    Yuzhen ** & Jilan Xu

Authors
  1. Jiawei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanchun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuzhen **
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jilan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. **aowei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiawei Zhang.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Zhang, Y., **, Y. et al. MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation. Health Inf Sci Syst 11, 13 (2023). https://doi.org/10.1007/s13755-022-00204-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13755-022-00204-9

Keywords

Search

Navigation