Skip to main content
SpringerLink
Log in

Rethinking the Encoder–decoder Structure in Medical Image Segmentation from Releasing Decoder Structure

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Medical image segmentation has witnessed rapid advancements with the emergence of encoder–decoder based methods. In the encoder–decoder structure, the primary goal of the decoding phase is not only to restore feature map resolution, but also to mitigate the loss of feature information incurred during the encoding phase. However, this approach gives rise to a challenge: multiple up-sampling operations in the decoder segment result in the loss of feature information. To address this challenge, we propose a novel network that removes the decoding structure to reduce feature information loss (CBL-Net). In particular, we introduce a Parallel Pooling Module (PPM) to counteract the feature information loss stemming from conventional and pooling operations during the encoding stage. Furthermore, we incorporate a Multiplexed Dilation Convolution (MDC) module to expand the network's receptive field. Also, although we have removed the decoding stage, we still need to recover the feature map resolution. Therefore, we introduced the Global Feature Recovery (GFR) module. It uses attention mechanism for the image feature map resolution recovery, which can effectively reduce the loss of feature information. We conduct extensive experimental evaluations on three publicly available medical image segmentation datasets: DRIVE, CHASEDB and MoNuSeg datasets. Experimental results show that our proposed network outperforms state-of-the-art methods in medical image segmentation. In addition, it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data Availability

The dataset used in this paper is publicly available.

References

  1. Zhan, B., Song, E., & Liu, H. (2023). FSA-Net: Rethinking the attention mechanisms in medical image segmentation from releasing global suppressed information. Computers in Biology and Medicine, 161, 106932. https://doi.org/10.1016/j.compbiomed.2023.106932

    Article  Google Scholar 

  2. Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298. https://doi.org/10.1109/TMI.2016.2528162

    Article  Google Scholar 

  3. Foruzan, A. H., Zoroofi, R. A., Sato, Y., & Hori, M. (2012). A Hessian-based filter for vascular segmentation of noisy hepatic CT scans. International Journal of Computer Assisted Radiology and Surgery, 7(2), 199–205. https://doi.org/10.1007/s11548-011-0640-y

    Article  Google Scholar 

  4. Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & Van Ginneken, B. (2004). Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 23(4), 501–509. https://doi.org/10.1109/TMI.2004.825627

    Article  Google Scholar 

  5. Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  6. 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, Boston, MA, USA (pp. 3431–3440). https://doi.org/10.1109/cvpr.2015.7298965

  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, Munich, Germany (pp. 234–241). https://doi.org/10.1007/978-3-319-24574-4_28

  8. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Proceedings 4, 2018, Granada, Spain (pp. 3–11). https://doi.org/10.1007/978-3-030-00889-5_1

  9. Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-assisted Intervention–MICCAI 2016: 19th International Conference, Proceedings, Part II 19, 2016, Athens, Greece (pp. 424–432). https://doi.org/10.1007/978-3-319-46723-8_49

  10. Ibtehaz, N., & Rahman, M. S. (2020). MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74–87. https://doi.org/10.1016/j.neunet.2019.08.025

    Article  Google Scholar 

  11. Guan, S., Khan, A. A., Sikdar, S., & Chitnis, P. V. (2019). Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal. IEEE Journal of Biomedical and Health Informatics, 24(2), 568–576. https://doi.org/10.1109/JBHI.2019.2912935

    Article  Google Scholar 

  12. Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955. https://doi.org/10.48550/arXiv.1802.06955

  13. Zhang, J., Li, C., Kosov, S., Grzegorzek, M., Shirahama, K., Jiang, T., Sun, C., Li, Z., & Li, H. (2021). LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation. Pattern Recognition, 115, 107885. https://doi.org/10.1016/j.patcog.2021.107885

    Article  Google Scholar 

  14. Ni, J., Liu, J., Li, X., & Chen, Z. (2022). SFA-Net: Scale and feature aggregate network for retinal vessel segmentation. Journal of Healthcare Engineering, 2022, 4695136. https://doi.org/10.1155/2022/4695136

    Article  Google Scholar 

  15. Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., & Wu, J. (2020). Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing 2020, Barcelona, Spain (pp. 1055–1059). https://doi.org/10.1109/icassp40776.2020.9053405

  16. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., & Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306. https://doi.org/10.48550/arXiv.2102.04306

  17. Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2022). Swin-unet: Unet-like pure transformer for medical image segmentation. In European Conference on Computer Vision (pp. 205–218). Springer. https://doi.org/10.1007/978-3-031-25066-8_9

  18. Ni, J., Sun, H., Xu, J., Liu, J., & Chen, Z. (2023). A feature aggregation and feature fusion network for retinal vessel segmentation. Biomedical Signal Processing and Control, 85, 104829. https://doi.org/10.1016/j.bspc.2023.104829

    Article  Google Scholar 

  19. Ni, J., Wu, J., Elazab, A., Tong, J., & Chen, Z. (2022). DNL-Net: Deformed non-local neural network for blood vessel segmentation. BMC Medical Imaging, 22(1), 1–14. https://doi.org/10.1186/s12880-022-00836-z

    Article  Google Scholar 

  20. Wu, H., Wang, W., Zhong, J., Lei, B., Wen, Z., & Qin, J. (2021). Scs-net: A scale and context sensitive network for retinal vessel segmentation. Medical Image Analysis, 70, 102025. https://doi.org/10.1016/j.media.2021.102025

    Article  Google Scholar 

  21. Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H. (2019). Dual attention network for scene segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Angeles CA, United States (pp. 3146–3154). https://doi.org/10.1109/CVPR.2019.00326

  22. Ni, J., Wu, J., Tong, J., Chen, Z., & Zhao, J. (2020). GC-Net: Global context network for medical image segmentation. Computer Methods and Programs in Biomedicine, 190, 105121. https://doi.org/10.1016/j.cmpb.2019.105121

    Article  Google Scholar 

  23. Ni, J., Wu, J., Wang, H., Tong, J., Chen, Z., Wong, K. K., & Abbott, D. (2020). Global channel attention networks for intracranial vessel segmentation. Computers in Biology and Medicine, 118, 103639. https://doi.org/10.1016/j.compbiomed.2020.103639

    Article  Google Scholar 

  24. Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., & Fan, C. (2021). Sa-unet: Spatial attention u-net for retinal vessel segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy (pp. 1236–1242). https://doi.org/10.1109/ICPR48806.2021.9413346

  25. Wang, C., Wang, Y., Liu, Y., He, Z., He, R., & Sun, Z. (2019). ScleraSegNet: An attention assisted U-Net model for accurate sclera segmentation. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2(1), 40–54. https://doi.org/10.1109/TBIOM.2019.2962190

    Article  Google Scholar 

  26. Woo, S., Park, J., Lee, J.-Y., & So Kweon, I. (2018). Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany (pp. 3–19). https://doi.org/10.1007/978-3-030-01234-2_1

  27. Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., Mcdonagh, S., Hammerla, N. Y., & Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999. https://doi.org/10.48550/arXiv.1804.03999

  28. Roy, A.G., Navab, N. & Wachinger, C. (2018). Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention, Granada, Spain (pp. 421–429). https://doi.org/10.1007/978-3-030-00928-1_48

  29. Qin, Y., Kamnitsas, K., Ancha, S., Nanavati, J., Cottrell, G., Criminisi, A., & Nori, A. (2018). Autofocus layer for semantic segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, Granada, Spain (pp. 603–611). https://doi.org/10.1007/978-3-030-00931-1_69

  30. Wang, Y., Deng, Z., Hu, X., Zhu, L., Yang, X., Xu, X., Heng, P.-A., & Ni, D. (2018). Deep attentional features for prostate segmentation in ultrasound. In: International Conference on Medical Image Computing and Computer-assisted Intervention, Granada, Spain (pp. 523–530). https://doi.org/10.1007/978-3-030-00937-3_60

  31. Zhu, H., Zeng, H., Liu, J., & Zhang, X. (2021). Logish: A new nonlinear nonmonotonic activation function for convolutional neural network. Neurocomputing, 458, 490–499. https://doi.org/10.1016/j.neucom.2021.06.067

    Article  Google Scholar 

  32. Chollet, F. (2017). Xception: Deep learning with depth wise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii (pp. 1251–1258). https://doi.org/10.1109/CVPR.2017.195

  33. Owen, C. G., Rudnicka, A. R., Mullen, R., Barman, S. A., Monekosso, D., Whincup, P. H., Ng, J., & Paterson, C. (2009). Measuring retinal vessel tortuosity in 10-year-old children: Validation of the computer-assisted image analysis of the retina (CAIAR) program. Investigative Ophthalmology and Visual Science, 50(5), 2004–2010. https://doi.org/10.1167/iovs.08-3018

    Article  Google Scholar 

  34. Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O. F., Tsougenis, E., Chen, H., Heng, P.-A., Li, J., & Hu, Z. (2019). A multi-organ nucleus segmentation challenge. IEEE Transactions on Medical Imaging, 39(5), 1380–1391. https://doi.org/10.1109/TMI.2019.2947628

    Article  Google Scholar 

Download references

Funding

This study was funded by the National Key Research and Development Program of China (Grant 2020YFB1708900) and the Fundamental Research Funds for the Central Universities (Grant No. B220201044).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, 241000, China

    Jiajia Ni

  2. School of Information Science and Engineering, HoHai University, Changzhou, 213000, China

    Jiajia Ni, Wei Mu, An Pan & Zhengming Chen

Authors
  1. Jiajia Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. An Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhengming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiajia Ni.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Ni, J., Mu, W., Pan, A. et al. Rethinking the Encoder–decoder Structure in Medical Image Segmentation from Releasing Decoder Structure. J Bionic Eng (2024). https://doi.org/10.1007/s42235-024-00513-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42235-024-00513-7

Keywords

Search

Navigation