Abstract
Due to the high similarity of organs in 3D liver image and the use of simple connection by U-Net to fuse different semantic features, the segmentation accuracy of network needs to be improved. To solve these problems, this paper proposes a 3D liver semantic segmentation method based on multi-scale feature fusion and coordinate attention mechanism. Firstly, in the encoder section of U-Net, the multi-scale feature fusion module was used to capture multi-scale features; Then, coordinate attention mechanism was used to fuse low-level features and high-level features to locate regions of interest; Finally, the segmentation effect of edge details was improved through a deep supervision mechanism. The experimental results show that: on the LiTS dataset, the dice similarity coefficient (DSC) of this method reaches 96.5%. Compared with the U3-Net + DC method, the DSC increases by 0.1%, and the relative volume difference (RVD) decreases by 1.09%; On the CHAOS dataset, the DSC of this method reaches 96.8%, and compared with CANet, the DSC increases by 0.2%; On the MRI dataset of a hospital, the DSC of this method reaches 97.2%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Muraleedharan, A., Ragavan, S., Nayakar, G.: Diagnostic Accuracy of Plain Computed Tomography (CT) to detect the Morphological Variations of Liver: An update (2023)
Zech, C.J., Ba-Ssalamah, A., Berg, T.: Consensus report from the 8th international forum for liver magnetic resonance imaging. Eur. Radiol. 30, 370–382 (2020)
Fu, M., Xu, P., Li, X.: Fast crowd density estimation with convolutional neural networks. Eng. Appl. Artif. Intell. 43, 81–88 (2015)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv, 1409–1556 (2014)
He, K., Zhang, X., Ren, S.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Las Vegas (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440. IEEE, Boston (2015)
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_28
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)
Huang, H., Lin, L., Tong, R.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059. IEEE, Barcelona (2020)
Li, X., Chen, H., Qi, X.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
Szegedy, C., Liu, W., Jia, Y.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. IEEE, Munich (2015)
Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J., Heng, P.-A.: 3D deeply supervised network for automatic liver segmentation from CT volumes. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 149–157. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_18
Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 13713–13722. IEEE, Kuala Lumpur (2021)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141. IEEE, Salt Lake (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Jiang, H., Shi, T., Bai, Z.: Ahcnet: an application of attention mechanism and hybrid connection for liver tumor segmentation in ct volumes. IEEE Access 7, 24898–24909 (2019)
Chen, Y., Wang, K., Liao, X.: Channel-Unet: a spatial channel-wise convolutional neural network for liver and tumors segmentation. Front. Genet. 10, 1110 (2019)
Lei, T., Zhou, W., Zhang, Y.: Lightweight v-net for liver segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1379–1383. IEEE, Barcelona (2020)
Wang, J., Lv, P., Wang, H.: SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in computed tomography. Comput. Methods Programs Biomed. 208, 106268 (2021)
Fan, T., Wang, G., Li, Y.: Ma-net: a multi-scale attention network for liver and tumor segmentation. IEEE Access 8, 179656–179665 (2020)
Jin, Q., Meng, Z., Sun, C.: RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans. Frontiers in Bioeng. Biotechnol. 8, 1471 (2020)
Tran, S.T., Cheng, C.H., Liu, D.G.: A multiple layer U-Net, Un-Net, for liver and liver tumor segmentation in CT. IEEE Access 9, 3752–3764 (2020)
Sinha, A., Dolz, J.: Multi-scale self-guided attention for medical image segmentation. IEEE J. Biomed. Health Inform. 25(1), 121–130 (2020)
Khan, R.A., Luo, Y., Wu, F.X.: RMS-UNet: residual multi-scale UNet for liver and lesion segmentation. Artif. Intell. Med. 124, 102231 (2022)
Mourya, G.K., Gogoi, M., Talbar, S.N.: Cascaded dilated deep residual network for volumetric liver segmentation from CT image. In: Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, pp. 1153–1165. IGI Global (2023)
Shao, S., Zhang, X., Cheng, R.: Semantic segmentation method of 3D liver image based on contextual attention model. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3042–3049. IEEE Melbourne (2021)
Zheng, S., Zhang, X.L., Deng, H.: 3D liver image segmentation method based on multi-scale feature fusion and grid attention mechanism. J. Computer Appl. (2022)
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (No. 61972299, 62071456).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, M., Zhang, X., Deng, H., Ren, H. (2023). A Segmentation Method of 3D Liver Image Based on Multi-scale Feature Fusion and Coordinate Attention Mechanism. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_1
Download citation
DOI: https://doi.org/10.1007/978-981-99-4749-2_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4748-5
Online ISBN: 978-981-99-4749-2
eBook Packages: Computer ScienceComputer Science (R0)